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In this paper, the author tested the influence of the addition of 1200 g of steel fibers per every beam improve the mechanical characteristics of concrete. The idea to make a better concrete mix by...

An accurate and reliable forecast of biosurfactant production with minimum error is useful in any bioprocess engineering. Bacterial isolate FKOD36 capable of producing biosurfactant was isolated in this study and pre-inoculums was prepared from the agar slants in a small test tube and incubated at 30 °C for 24 h at 120 rpm. Due to inherent non-linearity characteristics of the data set in a bioprocess, conventional modeling techniques are not adequate for predicting biosurfactant production in a microbiological process. The main contribution of the study was to compare two soft-computing models, i.e., support vector regression (SVR) and support vector regression coupled with firefly algorithm to evaluate the best performance of the two mentioned models. Based on the results it was noted that support vector regression coupled with firefly algorithm performs better compared to the simple SVR.

In this paper, the potential of adaptive neuro fuzzy inference system (ANFIS) was appraised for investigation of thermal performances of a minichannel heat sink for cooling of electronics using nanofluid coolant instead of pure water. The process, which simulates the thermal performances with ANFIS network, was constructed. The developed ANFIS network was with three neurons in the input layer, and one neuron (thermal performances) in the output layer. Since there are three thermal performances investigated, three ANFIS networks are created. The inputs were flow rate, as well as the Reynolds number. The third input represents the nanofluid concentration. The Al2O3–H2O nanofluid including the volume fraction was used as a coolant. Obtained experimental results showed the higher improvement of the thermal performances using nanofluid instead of pure distilled water. ANFIS results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach for heat sink base temperature prediction.

The paper presents the use of the difference GMM and the system GMM for the purpose of determining the critical determinants of non-performing loans. The aim of the paper is to point out the theoretical advantages of GMM in relation to other (often used) panel data estimators in the study of NPLs, and to demonstrate its advantages in the case of the emerging markets in the countries of Latin America. The paper seeks to identify a relevant econometric model in order to demonstrate the impact of crucial macro and microeconomic variables on the NPLs. No evidence was found in support of the significance of the impact of the inflation rate and the microeconomic variables that were the subject of research in this paper.

Information system development is a complex endeavor, one which is fraught with peril if one do not follow a proven method. The Rational Unified Process (RUP) is one such method which could help to organize the development process of any information system. The RUP takes an evolutionary approach to development which has been shown to be far more effective than the traditional approach which is based on simple procedure. Unified Modeling Language (UML) is a RUP graphical language for visualizing, specifying, constructing, and documenting the artifacts of a information system. UML offers a standard way to write a system’s blueprints which is crucial for any information system development. The main goal of the study is to develop electronic patent (e-patent) system by RUP approach and UML standard tools. Afterwards artificial neural networks are applied to predict potential economic growth (GDP) based on science factors including patents. Extreme learning machine (ELM) is used for training process of the networks. Extreme learning machine (ELM) is a class of single-hidden layer feedforward neural network (SLFN). In theory, ELM algorithm tends to provide good generalization performance at extremely fast learning speed. Experimental results show that ELM has very high correlation with current economic dispatch.

Purpose This paper aims to show the implementation in the terrestrial trunked radio (TETRA)-based sensor network. The publicly available data show that, in Serbia, the annual damage caused by hailstorms in the past seven years has been estimated almost at an average level of 40m of euros. As the amount of hail was not changed, the hail suppression system of the Republic of Serbia has to be improved, both technically and organizationally, to get better efficiency and protection and to reduce the damage. Design/methodology/approach In this paper, the authors show the implementation and improvements in the modern terrestrial trunked radio (TETRA)-based sensor network, and they propose the scientific use of sensors for remote control of automatic hail suppression rocket stations. Findings The authors’ idea is that TETRA should be used as an operational and official telecommunicating system for hail suppression activities units. A number of sensors, connected in a network, are used to maintain a high-quality functioning of this digital radio system, managed remotely and controlled either by operators or automatically. Originality/value The presented study with a real example attempts to explain as to how the system functions and how it can improve hail suppression activities.

Horizontal-axis wind turbines (HAWT) have the constant rotor speed, while the blade tip speed changes continuously. This could reduce power performance of the wind turbine. In this paper, the accuracy of soft-computing technique was employed for aerodynamics performance prediction based on continuously variable-speed horizontal-axis wind turbine with optimal blades. The process, which simulates the $$\varphi$$ (relative wind angle), BEP (blade element parameter), SP (solidity parameter), CPtot (total power coefficient), CPl (local power coefficient), and CT (local thrust coefficient), with adaptive neuro-fuzzy inference system (ANFIS) was constructed. The inputs were local speed ratios λr and different values of drag-to-lift ratio e. The performance of proposed system is confirmed by the simulation results. The ANFIS results are compared with the experimental results using root-mean-square error and coefficient of determination and Pearson’s coefficient. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The effectiveness of the proposed strategies is verified based on the simulation results.

In this paper was investigated the effect of exchange rate pass-through (ERPT) into aggregate import prices. The results shown in the long run pass-through was not observed. The results of our research confirm the results of previous investigations according to which pass-through is somewhat higher in transition countries than in developed ones and the pass-through degree is declining. Afterwards exchange rate effect on gross domestic product (GDP) was investigated by adaptive neuro fuzzy inference system (ANFIS). Dataset was collected from EUROSTAT database for 37 countries. The dataset was divided into two sets, for training and for testing of the ANFIS model. The main goal was to predict GDP in relation to exchange rate. ANFIS model was created by three Bell shaped membership functions since it was the best model according to the obtained results. Linear membership functions was used for output. Results shown that the ANFIS model can be used for GDP prediction based on exchange rate values.

In this paper the comparative results of on-site analysis and modelling of the mechanisms and kinetics of the mining waters in the ore deposit Crnac are presented. The research process included hydrogeology explorations on aquifer types, ground water occurrences, and the process of the surface water penetration considering the rock porosity on the confined bed. The mineralogy and microstructure analysis of the ore body are performed through the scanning electron microscopy–energy dispersive spectrometry (SEM-EDS) and X-ray diffraction (XRD) analysis before and after the mining process. Results proved the increased rate of pyrite decomposition due to the oxidation reaction in disturbed ore body and better exposure to the air.

Effective chemical protection of fruit is a complex task which can enable production of healthy food without chemical residues. As food health is a growing global concern, it is important to automate and optimize the fruit protection process. Different data mining techniques can be used to identify pattern diseases so as to prevent the excessive use of chemicals. However, application of data mining systems in this field is very complex task. Besides that, these systems are often designed for just one specific plant species. One solution for prediction a risk of fruit infection based on data that represent weather (meteorological) conditions and data pathogens is presented in this paper. The research is performed on the data collected at the region of Toplica in Republic of Serbia during five year period. In this paper data mining based tool for early prediction of fruit pathogen infection is performed. The application is based on the open source engine WEKA with GUI created in C#, and uses several data mining algorithms which are evaluated in this paper. Results shown that the prediction accuracy is 89%.

Energy consumption could has great impact of economic development and on carbon dioxide emission (CO2) intensity. However there are many possibilities to avoid these undesirable problems. One of the solution is to use more alternative and renewable energy sources but CO2 emission intensity from the other sources need to be investigated more. Therefore in this article was analyzed the CO2 emission intensity based on the alternative, fossil and renewable energy. It was analyzed also the economic growth based on the CO2 emission intensity. Neuro-fuzzy method was applied to select the most impactful energy factor for the CO2 emission intensity and economic growth. Presented results confirmed that the alternative energy has the highest influence on the CO2 emission intensity. CO2 emissions intensity from solid fuel has the highest influence on the economic growth.

Today’s world depends largely on information and communication technologies. These technologies are in use in different areas of human life and work. Each day, more examples of possible application of information and communications technology are being discovered. In most cases, computer science is used to solve complex problems which have mathematical background. The most important and challenging job in agriculture is plant protection. This is due to its complexity and the lack of specialized tools that could predict when the conditions for specific infections are fulfilled. In this paper authors use different mathematics-based techniques for data processing and prediction of possible fruit disease infection. Six significant weather variables and one variable representing the month in the year are selected as predictor variables. Implemented techniques are compared with each other in order to select the best. Prediction includes two most important diseases of cherry fruit: monilinia laxa and coccomyces hiemalis. Data sets used in this research include data of eight year time period, collected over the region of Toplica in Serbia. The best achieved prediction accuracy is 95.8%. Additionally, the same implemented methods can be applied on other fruit species and other diseases for which data are known.

Evapotranspiration of important indicator for management and planning of water resources. It is essential to analyze the evapotranspiration in order to improve water resources planning. The main goal of the study was to analyze the evapotranspiration based on several input parameters. It is important to estimate the influence of the input parameters on the evapotranspiration. For such a purpose evolutionary algorithm was applied. The algorithm applied in this article has space solution of genetic programs. Therefore this methodology is known as genetic programming. The input parameters in the model are monthly minimum and maximum air temperatures, sunshine hours, actual vapour pressure, minimum and maximum relative humidity and wind speed. Results presented in this study could be used for practical application of water resources planning and management based on the input parameters influence on the evapotranspiration.

Purpose The main aim of the study was to measure the cutting forces and chip shapes based on different machining parameters. Design/methodology/approach To get the best optimal machining conditions, it is essential to use the best combination of machining parameters. Although some machining parameters are not important for the process, there are machining parameters which are very important for the machining process. Findings It is essential to determine which machining parameters are the most dominant to make the optimal machining conditions. Originality/value Six different chip shapes are obtained according to ISO standardization. It was determined that the different cutting forces occurred for the different chip shapes.

Evaluation of the optimal laser cutting parameters is very important for the high cut quality. This is highly nonlinear process with different parameters which is the main challenge in the optimization process. Data mining methodology is one of most versatile method which can be used laser cutting process optimization. Support vector regression (SVR) procedure is implemented since it is a versatile and robust technique for very nonlinear data regression. The goal in this study was to determine the optimal laser cutting parameters to ensure robust condition for minimization of average surface roughness. Three cutting parameters, the cutting speed, the laser power, and the assist gas pressure, were used in the investigation. As a laser type TruLaser 1030 technological system was used. Nitrogen as an assisted gas was used in the laser cutting process. As the data mining method, support vector regression procedure was used. Data mining prediction accuracy was very high according the coefficient (R2) of determination and root mean square error (RMSE): R2 = 0.9975 and RMSE = 0.0337. Therefore the data mining approach could be used effectively for determination of the optimal conditions of the laser cutting process.

Purpose The main purpose of the study was to analyze the relationship between cutting forces and tool wear during turning of steel 30CrNiMo8. Design/methodology/approach It is very important to find the optimal machining conditions to increase the tool life and to improve product quality. Width of tool wear was measured by universal microscope. Findings During experimental procedure, one chip shape was obtained for the given machining parameters. Results showed negligible tool wear for the given experimental conditions. In other words, the tool wear is negligible for one chip shape. Originality/value To increase tool wear, there are different chip shapes.

Purpose This study aims to detect the temperature distribution in the cutting zone during the machining process. Furthermore, temperature influence in the cutting zone on the forms of chip shapes during the turning of Steel 30CrNiMo8 was evaluated. It is very important to use optimal machining parameters to get the best production results or for high control of the machining process. Design/methodology/approach Temperature distribution in the cutting zone during the machining process could affect the forms of chip shapes. Forms of chip shapes could be considered as the most important indicator for the quality of the machining process. Findings Therefore, in this study, the forms of chip shapes based on the temperature distribution in the cutting zone were examined. Originality/value It was found that the snarled chip type and the loose chip type have the highest temperature variation during the machining process.

Purpose The paper aims to present an investigation of wood bonding strength as a very important indicator for effective using in further manufacturing processes. Design/methodology/approach In this study, the wood bonding strength sensitivity was estimated based on grain orientation, feed speed, heating time and temperature, temperature and type of adhesive. Artificial intelligence methods were applied for this analysis because it is strongly a nonlinear process. Findings It was shown that the artificial intelligence tools could be useful, reliable and effective for the wood bonding strength sensitivity estimation. Afterwards the power consumption in in solid wood machining process is analyzed and estimated by the artificial intelligence tools. Originality/value Results shown that the wood bonding strength is the most sensitive for type of adhesive. Thus, the results of the present research can be successfully applied in the wood industry to reduce the time, energy and high experimental costs.

Purpose Acoustic emission (AE) could be used for prevention and detection of tool errors in Computer Numerical Control (CNC) machining. The purpose of this study is to analyze the AE form of CNC machining operations. Design/methodology/approach Experimental measurements were performed with three sensors on the CNC lathe to collect the data of the CNC machining. Adaptive neuro-fuzzy inference system (ANFIS) was applied for the fusion from the sensors’ signals to determine the strength of the signal periodic component among the sensors. Findings There were three inputs, namely, spindle speed, feed rate and depth of cut. ANFIS was also used to determine the inputs’ influence on the prediction of strength of the signal periodic component. Variable selection process was used to select the most dominant factors which affect the prediction of strength of the signal periodic component. Originality/value Results were shown that the spindle speed has the most dominant effect on the strength of the signal periodic component.

The oscillating elements obradog system in principle is conditional on the dynamic character of the process of formation filings. It is necessary to let the frequency of natural oscillation elements of the system tool - workpiece - accessories - machine is approximately equal to the frequency of forming segments filings to lead to the emergence of chatter (Landberg). Studies have found that the process of forming filings do not affect the characteristics of rigidity machines (Ferrarezzi). Today the world literature meets a large number of analytical and mechanistic models, with more or less accuracy realistically describe the real process of forming filings. In recent years, with the advent of powerful computers and application programs based on the finite element method (FEM) modeling of the dynamic processes of formation filings, as well as other processes that take place in the cutting zone, are the subject of numerical methods.

Adaptive grippers should be able to detect and recognize grasping objects. To be able to do it control algorithm need to be established to control gripper tasks. Compliant underactuated mechanisms with passive behavior can be used for modelling of adaptive robotic fingers. Undearactuation is a feature which allows fully adaptability of robotic fingers for different objects. In this study gripper with two fingers was established. Finite element method (FEM) procedure was used to optimize the gripper structural topology. Kinetostatic model of the underactuated finger mechanism was analyzed. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. The sensors could be used for grasping shape detection. Given that the contact forces of the finger depend on contact position of the finger and object, it is suitable to make a prediction model for the contact forces in function of contact positions of the finger and grasping objects. The prediction of the contact forces was established by using a soft computing (computational intelligence) approach. To perform the contact forces estimation adaptive neuro-fuzzy (ANFIS) methodology was used. FEM simulations were performed in order to acquire experimental data for ANFIS training. The main goal was to apply ANFIS network in order to find correlation between sensors’ stresses and finger contact forces. Afterwards ANFIS results were compared with benchmark models (extreme learning machine (ELM), extreme learning machine with discrete wavelet algorithm (ELM-WAVELET), support vector machines (SVM), support vector machines with discrete wavelet algorithm (SVM-WAVELET), genetic programming (GP) and artificial neural network (ANN)). The reliability of these computational models was analyzed based on simulation results.

Purpose This paper aims to explore detection of heating load of building by machine learning. Detection of heating load of building is very important in design of buildings due to efficient energy consumption. Design/methodology/approach In this study, detection of heating load of building based on effects of dry-bulb temperature, dew-point temperature, radiation, diffuse radiation and wind speed was analyzed. Machine learning approach was implemented for such a purpose. Findings The obtained results could be useful for future planning of heating load of buildings. Because the heating load of building is a very nonlinear phenomenon, it is suitable to use machine learning approach to avoid the nonlinearity of the system. Originality/value The obtained results could be used effectively in detection of heating load of buildings.

Purpose The purpose of the paper is to evaluate a lake lavel prediction model. Lake-level prediction is very important task for different crucial issues like planning of water resource, controlled drainage, etc. Therefore, in this study, a soft computing approach was applied to predict the lake levels based on the different prediction horizons. Design/methodology/approach The main focus was to establish a sensorless estimation of the lake level based on different prediction horizons. Support vector regression approach was implemented for the lake-level prediction, as this approach is suitable for highly nonlinear prediction problems. Findings Ludoš Lake in Serbia was used for analysis. Originality/value According the results, the soft computing models can be used confidently for the lake-level prediction based on the prediction accuracy.

In this paper, the accuracy of the Weibull model of wind speed is evaluated using an adaptive neuro-fuzzy inference system (ANFIS) based on wind data. The wind data comprises of wind speed measurements in the city of Nis in Serbia at different heights of 10 m, 30 m and 40 m for duration of one year. The ANFIS results are compared with the experimental results and Weibull model using root-mean-square error (RMSE), coefficient of determination, and Pearson coefficient. The effectiveness of the proposed unified strategy is verified based on the simulation results.

The potential of marine power to produce electricity has been exploited recently. One of the ways of producing electricity is by using tidal current turbines. In this study, the hydrodynamic performance of a novel type of ducted tidal turbine was investigated. Tidal energy has become a large contender of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized tidal turbines. Hence, quality of produced energy becomes an important problem in tidal energy conversion plants. Several control techniques have been applied to improve the quality of power generated from tidal turbines. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate power coefficient value of the ducted tidal turbines. The back propagation learning algorithm is used for training this network. This intelligent controller is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Purpose The paper aims to analyze chip formation during machining process since it can be a very important indicator for the quality of the machining process, as some chip forms can be undesirable. Design/methodology/approach It is essential to determine the sensitivity of the chip formation on the basis of different machining parameters. The main goal of the study was to analyze the sensitivity of the chip formation during the machining process by using adaptive neuro-fuzzy inference system (ANFIS). Findings According to the results, the chip formation is the most sensitive to feed rate. Originality/value Different cutting tests were performed to monitor the chip formation on the basis of the cutting forces and the cutting displacement. ANFIS was used to estimate the sensitivity of the chip formation during the cutting process on the basis of different parameters.

The estimation of the water cycle.Forecasting of evapotranspiration.The accuracy of the neuro-fuzzy approach. Water cycle shows the continuous movement of water above and below surface. The water moves could involves the energy exchange which can lead to temperature changes. It is crucial to elaborate the energy exchange in relation to climate changing. Evapotranspiration is one of the most important part of the water cycle. Evaporation presents the water movement to the air and transpiration presents the water movements within a plant. Since the evapotranspiration is very important parameter for climate change, in this article the main aim was to estimate the evapotranspiration based on different climatic parameters such as air temperature, vapor pressure and humidity. Neuro-fuzzy approach was used for the process modeling since the evapotranspiration is very unpredictable factor with strong fluctuation through year. The results could be used for evapotranspiration estimation based on the climate data in order to improve water resources management for agricultural production and irrigation scheduling.

Thermal sensation of visitors is very important factor for open urban spaces for physiological, psychological and behavioral conditions of the visitors. Therefore in this article thermal sensation of the visitors in open urban spaces is investigated. For such a purpose physical data are used and questionnaire study as well. Sensitivity analysis is performed on the thermal sensation of the visitors based on different factors. ANFIS (adaptive neuro fuzzy inference system) methodology is applied for the analysis. The main ability of the methodology is its adaptive behavior. The main goal is to investigate how the visitors use the open urban areas for their activity. Results are shown that the temperature and solar radiation are the factors with the highest impact on the visitors' thermal sensation in the open urban spaces.

Thermal comfort in open urban areas could influence medical, sociological, environmental and economic parameters. Therefore there is need to establish good thermal comfort in the areas to maintain the parameters at acceptable levels. Since the thermal comfort is variable, there is need to determine which parameters has the most impact on the thermal comfort variations. Hence in this article an attempt was made to rank parameters based on their influence on the thermal comfort. Prediction of thermal comfort was performed based on the parameters' ranking models. Physiological equivalent temperature (PET) was used as the thermal comfort indicator. Air temperature, vapor pressure, wind speed, irradiance are the inputs which are used in the investigation among others. Neuro-fuzzy methodology was used for the PET analyzing. Obtained results could have practical advantage for urban planners and designers to make the best thermal comfort for visitors in open urban areas.

This paper analyses motion trajectory of vibro-impact system based on the oscillator moving along the rough parabolic line in the vertical plane, under the action of external single-frequency force. Nonideality of the bond originates of slidingCoulomb’stype friction force with coefficientμ=tgα0. The oscillator consists of one heavy mass particle whose forced motion is limited by two angular elongation fixed limiters. The differential equation of motion of the analyzed vibro-impact system, which belongs to the group of common second order nonhomogenous nonlinear differential equations, cannot be solved explicitly (in closed form). For its approximate solving, the software package WOLFRAM Mathematica 7 is used. The results are tested by using the software package MATLAB R2008a. The combination of analytical-numerical results for the defined parameters of analyzed vibro-impact system is a base for the motion analysis visualization, which was the primary objective of this analytic research. Upon the phase portrait of the heavy mass particle obtained, the energy of the considered vibro-impact system is analyzed. During the graphical visualization of the energetic changes, one of the steps is the process of the phase trajectory equations determination. For this determination, we have used interpolation process that utilizesLagrangeinterpolation polynomial.

Forced vibro-impact dynamics of the two heavy mass particle motions, in vertical plane, along rough circle with Coulomb’s type friction and one, one side impact limiter is considered in combinations of applied analytical and numerical methods. System of two differential double equations, each for one of two heavy mass particle motions along same rough circle are composed with corresponding initial conditions as well as impact conditions. By use software package tools differential double equations are numerically integrated for obtaining phase portrait of phase trajectory branches for different mass particles initial kinetic states. By series of the phase trajectory branches for each mass particle motion between two impacts or between impact and alternation of the Coulomb’s friction force direction, two phase trajectory graphs of the system vibro-impact non-linear dynamics are composed. Different software tools are used as helping tools for calculate time moments of the series of the impacts between mass particles, as well as positions of the impacts, necessary for calculations of the impact velocities of the mass particles before and after impacts. Some comparison between forced and free vibro-impact dynamics of the two heavy mass particles in vertical plane, along rough circle with Coulomb’s type friction and one, one side impact limiter is done. Trigger of coupled one side singularities in phase portraits are identified.