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 article deals with issues related to the application of statistical methods used in the valuation process. The proposed algorithm for real estate valuation can be used in the statistical market analysis method in the process of mass appraisal. The algorithm uses a multiple linear regression model. Legal considerations indicate the need for such an algorithm for the determination of the value of representative properties. Due to the large size of the database of comparables, the proposed algorithm can be used only to appraise typical properties. A good statistical model is parsimonious, that is, it uses as few mathematical concepts as possible in a given situation. A model should extract what is systematic in the results observed, allowing for the presence of purely random deviations. The article discusses the basic principles of building a good statistical model. Attention is drawn to the number of market attributes that are entered into the model and the range of their values. As few explanatory variables as possible should be entered into the model to explain the phenomenon under study. Explanatory variables are only those characteristics of the property that differentiate prices in a given market defined and adopted by the appraiser as the basis for valuation. The article highlights the importance of taking into account market changes during the period under study.

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.

This paper presents a study on the dry turning of polyoxymethylene copolymer POM-C. The effect of five factors (cutting speed, feed rate, depth of cut, nose radius, and main cutting edge angle) on machinability is evaluated using four output parameters: surface roughness, tangential force, cutting power, and material removal rate. To do so, the study relies on three approaches: (i) Pareto statistical analysis, (ii) multiple linear regression modeling, and (iii) optimization using the genetic algorithm. To conduct the investigation, mathematical models are developed using response surface methodology based on the Taguchi L16 orthogonal array. The results indicate that feed rate, nose radius, and cutting edge angle significantly influence surface quality, while depth of cut, feed, and speed have a notable impact on other machinability parameters. The developed mathematical models have determination coefficients greater than or very close to 95%, making them very useful for the industry as they allow predicting response values based on the chosen cutting parameters. Finally, the optimization using the genetic algorithm proves to be promising and effective in determining the optimal cutting parameters to maximize productivity while improving surface quality.

Meteorological parameters which are most significant for ozone forecasting were chosen in the multiple regression analysis for the daily time series. Then correlations between the variables we~e investigated, both for the daily and temporary values. There was confirmed a strong relationship between atmospheric conditions and ozone concentrations as well as autocorrelations of the temporary time series of ozone from different monitoring stations. Diversification of autocorrelation values arises probably from different receptor locations which was confirmed by the principal component analysis. There were also shown dependences between the ozone time series from different monitoring stations. Strong space-time relationships of ozone concentrations and meteorological conditions in the Black Triangle region can be used in modeling and forecasting of ozone episodes.

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.

A multiple regression model approach was developed to estimate buffering indices, as well as biogas and methane productions in an upflow anaerobic sludge blanket (UASB) reactor treating coffee wet wastewater. Five input variables measured (pH, alkalinity, outlet VFA concentration, and total and soluble COD removal) were selected to develop the best models to identify their importance on methanation. Optimal regression models were selected based on four statistical performance criteria, viz. Mallow’s Cp statistic (Cp), Akaike information criterion ( AIC), Hannan– Quinn criterion ( HQC), and Schwarz–Bayesian information criterion ( SBIC). The performance of the models selected were assessed through several descriptive statistics such as measure of goodness-of-fit test (coefficient of multiple determination, R2; adjusted coefficient of multiple determination, Adj-R2; standard error of estimation, SEE; and Durbin–Watson statistic, DWS), and statistics on the prediction errors (mean squared error, MSE; mean absolute error, MAE; mean absolute percentage error, MAPE; mean error, ME and mean percentage error, MPE). The estimated model reveals that buffering indices are strongly influenced by three variables (volatile fatty acids (VFA) concentration, soluble COD removal, and alkalinity); while, pH, VFA concentration and total COD removal were the most significant independent variables in biogas and methane production. The developed equation models obtained in this study, could be a powerful tool to predict the functionability and stability for the UASB system.

The paper presents a methodology of modeling relationships between chemical composition and hardenability of structural alloy steels using computational intelligence methods, that are artificial neural network and multiple regression models. Particularly, the researchers used unidirectional multilayer teaching method based on the error backpropagation algorithm and a quasi-newton methods. Based on previously known methodologies, it was found that there is no universal method of modeling hardenability, and it was also noted that there are errors related to the calculation of the curve. The study was performed on large set of experimental data containing required information on about the chemical compositions and corresponding Jominy hardenability curves for over 400 data steel heats with variety of chemical compositions. It is demonstrated that the full practical usefulness of the developed models in the selection of materials for particular applications with intended performance in the area of application.

The paper’s objective was to present the results of predicting the stiffness modulus of a recycled mix containing a blended road binder with foamed bitumen and emulsified bitumen. The Sm (acc. to IT-CY) indirect tensile test was used at temperatures of -10°C, 5°C, 13°C and 25°C. Prediction of the stiffness modulus accounted for the effect of temperature, the type of road binders, the sampling location and the type of technology selected. All effects, except temperature, were included in the model by entangling their effects through recycled base course physical and mechanical characteristics, such as indirect tensile strength, compressive strength, creep rate, air void content and moisture resistance. As a result, it was possible to determine a regression model based on multiple regression with a coefficient of determination R² = 0.78. Temperature and compressive strength were found to have the strongest effect on the variability of stiffness modulus. However, indirect tensile strength also significantly affected the Sm characteristic. In addition, FB-RCM (foamed bitumen) recycled mixtures proved to be more favourable than EB-RCM (emulsified bitumen) mixtures as they exhibited a lower deformation rate while retaining limited stiffness.