Light-weight Self-Compacting Concrete (LWSCC) might be the answer to the increasing construction requirements of slenderer and more heavily reinforced structural elements. However there are limited studies to prove its ability in real construction projects. In conjunction with the traditional methods, artificial intelligent based modeling methods have been applied to simulate the non-linear and complex behavior of concrete in the recent years. Twenty one laboratory experimental investigations on the mechanical properties of LWSCC; published in recent 12 years have been analyzed in this study. The collected information is used to investigate the relationship between compressive strength, elasticity modulus and splitting tensile strength in LWSCC. Analytically proposed model in ANFIS is verified by multi factor linear regression analysis. Comparing the estimated results, ANFIS analysis gives more compatible results and is preferred to estimate the properties of LWSCC.

In this study, the aim was to model the toxic effect of copper (Cu) and analyse the removal of Cu in aqueous Saharan and non-Saharan mediums by Lemna minor. Two separate test groups were formed: with Saharan dust (S) and without Saharan dust (WS). These test groups were exposed to 3 different Cu concentrations (0.05, 0.50 and 5.00 ppm). Time, concentration, and group-dependent removal effi ciencies were compared using the non-parametric Mann-Whitney U test and statistically signifi cant differences were found. The optimum removal values were tested at the highest concentration 79.6% in the S medium and observed on the 4th day for all test groups. The lowest removal value (16%) was observed at 0.50 ppm on the 1st day in the WS medium. When the S medium and WS medium were compared, in all test groups Cu was removed more successfully in the S medium than the WS medium contaminated by Cu in 3 different concentrations of (0.05 ppm, 0.50 ppm, 5.00 ppm). The regression analysis was also tested for all prediction models. Different models were performed and it was found that cubic models show the highest predicted values (R²). The R² values of the estimation models were found to be at the interval of 0.939–0.991 in the WS medium and 0.995–1.000 in the S medium.

Mechanical properties of aluminum-silicon alloys are defined by condition of alloying components in the structure, i.e. plastic metallic matrix created from solid solution  on the basis of Al, as well as hard and brittle precipitations of silicon. Size and distribution of silicon crystals are the main factors having effect on field of practical applications of such alloys. Registration of crystallization processes of the alloys on stage of their preparation is directly connected with practical implementation of crystallization theory to controlling technological processes, enabling obtainment of suitable structure of the material and determining its usage for specific requirements. An attempt to evaluate correlation between values of characteristic points laying on crystallization curves and recorded with use of developed by the author TVDA method (commonly denominated as ATND method) is presented in the paper together with assessment of hardness of tested alloy. Basing on characteristic points from the TVDA method, hardness of EN AC-AlSi9Mg alloy modified with strontium has been described in the paper in a significant way by the first order polynomial.

Maternal mortality has posed a great problem in the health sector of most African countries. Nigeria’s maternal mortality ratio remains high despite efforts made to meet millennium development goal 5 (MDG5). This study used the Lagos state community health survey 2011 and the Lagos state health budget allocations 2011 to examine the effect of government expenditure on maternal mortality ratio. Factors like inadequate transportation facilities, lack of awareness, inadequate infrastructures, which contribute to high maternal mortality rate, can be traced back to revenue though under different ministries. The other ministries need to work and support the ministry of health in the fight against maternal, especially in Lagos state. Secondary data was compiled from the state budget, records of death in different local governments in the state and relevant reviewed literature. Regression analysis was used to analyze the hypothesis and it was discovered that government expenditure does not have a significant effect on maternal mortality based on the R-square coefficient. However, correlation coefficient gives a contrasting result. Hence, further research work, government expenditure from other local government areas need to be taken into consideration to arrive at a valid conclusion. It is difficult to ascertain how much of the revenue allocated was put to appropriate use, due to a high level of corruption.

The selection of the formwork system for high rise building affects the entire construction project duration and cost. The study reports the factors influencing the selection of different formwork system in the construction of high rise buildings through structural questionnaire survey from the client, contractor, consultant, and interviews with expert members. Total of 40 technical factors was identified from the literature and 220 filled questionnaires were received from the respondent. Relative Importance Index method is used to find the topmost factors affecting the selection of formwork system. Additionally, from factor analysis 22 factors were identified to have a correlation with one another. Regression analysis reveals that duration of the project, maintenance cost, adaptability, and safety have impact on formwork selection across time, cost and quality. These findings could potentially increase the construction company’s existing knowledge in relation to formwork selection.

In this paper, we randomly select 75 sets data of calcium sulfate hemihydrate (CSH) content and initial setting time, and the traditional test method of CSH and analyses initial setting time was used by complexometric titration. So the close relationship between them was studied in depth, which classification fitting data to be analyzed by regression analysis. The result shows that this regression analysis method can accurately determine CSH content in modified industrial by-product gypsum. The determination method has the advantages of simplification and rapid operation. As well as, the XRF quantitative analytical method was used to test the CSH content, which verified the accuracy of regression analysis method. The results also show that this method has high accuracy, and can simplify the traditional experimental process. The method developed is easier and more convenient and has broad prospects in application.

The research was concerned with the influence of chemical composition of austenitic steels on their mechanical properties. Resulting properties of castings from austenitic steels are significantly influenced by the solidification time that affects the size of the primary grain as well as the layout of elements within the dendrite and its parts with regard to the last solidification points in the interdendritic melt. During solidification an intensive segregation of all admixtures occurs in the melt, which causes a whole range of serious metallurgical defects and it has also a significant influence on subsequent precipitation of carbides and intermetallic phases. Chemical heterogeneity then affects the structure and mechanical properties of the casting. In a planned experiment, we cast melted steels containing 18 to 28 % Cr and 8 to 28 % Ni with variable carbon and nitrogen contents. Testing the tensile strength of the cast specimens we could determine the Rp0.2, Rm, and A5 values. The dependence of the mechanical properties on the chemical content was described by regression equations. The planned experiment results allow us to control the chemical content for the given austenitic steel quality to achieve the required values of the mechanical properties.

The purpose of this study is to identify relationships between the values of the fluidity obtained by computer simulation and by an experimental test in the horizontal three-channel mould designed in accordance with the Measurement Systems Analysis. Al-Si alloy was a model material. The factors affecting the fluidity varied in following ranges: Si content 5 wt.% – 12 wt.%, Fe content 0.15 wt.% – 0.3wt. %, the pouring temperature 605°C-830°C, and the pouring speed 100 g · s–1 – 400 g · s–1. The software NovaFlow&Solid was used for simulations. The statistically significant difference between the value of fluidity calculated by the equation and obtained by experiment was not found. This design simplifies the calculation of the capability of the measurement process of the fluidity with full replacement of experiments by calculation, using regression equation.

During the machining processes, heat gets generated as a result of plastic deformation of metal and friction along the tool–chip and tool–work piece interface. In materials having high thermal conductivity, like aluminium alloys, large amount of this heat is absorbed by the work piece. This results in the rise in the temperature of the work piece, which may lead to dimensional inaccuracies, surface damage and deformation. So, it is needed to control rise in the temperature of the work piece. This paper focuses on the measurement, analysis and prediction of work piece temperature rise during the dry end milling operation of Al 6063. The control factors used for experimentation were number of flutes, spindle speed, depth of cut and feed rate. The Taguchi method was employed for the planning of experimentation and L18 orthogonal array was selected. The temperature rise of the work piece was measured with the help of K-type thermocouple embedded in the work piece. Signal to noise (S/N) ratio analysis was carried out using the lower-the-better quality characteristics. Depth of cut was identified as the most significant factor affecting the work piece temperature rise, followed by spindle speed. Analysis of variance (ANOVA) was employed to find out the significant parameters affecting the work piece temperature rise. ANOVA results were found to be in line with the S/N ratio analysis. Regression analysis was used for developing empirical equation of temperature rise. The temperature rise of the work piece was calculated using the regression equation and was found to be in good agreement with the measured values. Finally, confirmation tests were carried out to verify the results obtained. From the confirmation test it was found that the Taguchi method is an effective method to determine optimised parameters for minimization of work piece temperature.

Geometric deviations of free-form surfaces are attributed to many phenomena that occur during machining, both systematic (deterministic) and random in character. Measurements of free-form surfaces are performed with the use of numerically controlled CMMs on the basis of a CAD model, which results in obtaining coordinates of discrete measurement points. The spatial coordinates assigned at each measurement point include both a deterministic component and a random component at different proportions. The deterministic component of deviations is in fact the systematic component of processing errors, which is repetitive in nature. A CAD representation of deterministic geometric deviations might constitute the basis for completing a number of tasks connected with measurement and processing of free-form surfaces. The paper presents the results of testing a methodology of determining CAD models by estimating deterministic geometric deviations. The research was performed on simulated deviations superimposed on the CAD model of a nominal surface. Regression analysis, an iterative procedure, spatial statistics methods, and NURBS modelling were used for establishing the model.

Electroencephalogram (EEG) is one of biomedical signals measured during all-night polysomnography to diagnose sleep disorders, including sleep apnoea. Usually two central EEG channels (C3-A2 and C4- A1) are recorded, but typically only one of them are used. The purpose of this work was to compare discriminative features characterizing normal breathing, as well as obstructive and central sleep apnoeas derived from these central EEG channels. The same methodology of feature extraction and selection was applied separately for the both synchronous signals. The features were extracted by combined discrete wavelet and Hilbert transforms. Afterwards, the statistical indexes were calculated and the features were selected using the analysis of variance and multivariate regression. According to the obtained results, there is a partial difference in information contained in the EEG signals carried by C3-A2 and C4-A1 EEG channels, so data from the both channels should be preferably used together for automatic sleep apnoea detection and differentiation.

To achieve better precision of features generated using the micro-electrical discharge machining (micro-EDM), there is a necessity to minimize the wear of the tool electrode, because a change in the dimensions of the electrode is reflected directly or indirectly on the feature. This paper presents a novel modeling and analysis approach of the tool wear in micro-EDM using a systematic statistical method exemplifying the influences of capacitance, feed rate and voltage on the tool wear ratio. The association between tool wear ratio and the input factors is comprehended by using main effect plots, interaction effects and regression analysis. A maximum variation of four-fold in the tool wear ratio have been observed which indicated that the tool wear ratio varies significantly over the trials. As the capacitance increases from 1 to 10 nF, the increase in tool wear ratio is by 33%. An increase in voltage as well as capacitance would lead to an increase in the number of charged particles, the number of collisions among them, which further enhances the transfer of the proportion of heat energy to the tool surface. Furthermore, to model the tool wear phenomenon, a egression relationship between tool wear ratio and the process inputs has been developed.

Electrical Discharge Machining (EDM) process with copper tool electrode is used to investigate the machining characteristics of AISI D2 tool steel material. The multi-wall carbon nanotube is mixed with dielectric fluids and its end characteristics like surface roughness, fractal dimension and metal removal rate (MRR) are analysed. In this EDM process, regression model is developed to predict surface roughness. The collection of experimental data is by using L9 Orthogonal Array. This study investigates the optimization of EDM machining parameters for AISI D2 Tool steel using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Analysis of variance (ANOVA) and F-test are used to check the validity of the regression model and to determine the significant parameter affecting the surface roughness. Atomic Force Microscope (AFM) is used to capture the machined image at micro size and using spectroscopy software the surface roughness and fractal dimensions are analysed. Later, the parameters are optimized using MINITAB 15 software, and regression equation is compared with the actual measurements of machining process parameters. The developed mathematical model is further coupled with Genetic Algorithm (GA) to determine the optimum conditions leading to the minimum surface roughness value of the workpiece.

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.

This publication presents the research aimed at developing statistical models, on the basis of which it was possible to prepare credible forecasts of unit cost and coal net output for longwalls in 5 hard coal mines in P oland. The argument has been verified that there is a dependence between the level of nuisance and the level of costs, as well as longwall production results.

A research procedure has been developed for that purpose, which aimed at developing two statistical models connecting the nuisance due to geological and mining conditions with costs and longwall production results. The multiple linear regression technique has been used to develop statistical models. The set of data taken into account in the analyses comprised 120 longwalls mined in the years 2010–2019. Two models have been developed – one for forecasting unit costs, the other for forecasting coal net output. Subsequently, the models’ forecasting ability has been verified on a sample of historical data. A relative forecast error for 75% of observations has been in the range of (–25%; +37%). That result has been considered satisfactory. Subsequently, using those models, forecasts of unit costs and coal net output have been prepared for 220 longwalls planned for mining in the years 2020–2030. Those forecasts have been prepared in the stipulated ranges of geological and mining nuisance influencing mining process, by means of dedicated W Ue and W Ut factors. The nuisance models for forecasting purposes have been developed using the AHP (Analytic Hierarchy Process) method. The research hypothesis has been confirmed on the basis of the obtained results. An increase in the level of nuisance leads to an increase in the unit costs for longwalls and the deterioration of production results. Unit operating costs for longwalls in specific ranges of nuisance may differ by up to 30%, being in the range of 52.0–120.3 zł/Mg. Likewise, the coal daily output of longwalls may be even 22% lower, having the average level in the range of 1.89–3.61 thousand Mg/d.

The content of this paper is dedicated to the analysis of the flat planarity of forklift stacker’s track and cross sections of lanes between racks in a warehouse. These results will serve as a basis for a possible reconstruction of the track and racks and shall contribute to the overall reduction of costs related to an unexpected bad technical condition. The contribution aims to assess the geometric parameters of warehouse racks at the selected company operation in terms of their suitability for further use. The choice of the selected topic represents a relevant issue, which can be possibly encountered in daily practice related to the storage and transport processes of products. The measurements and processing of longitudinal profiles and cross-sections were made in the local coordinate and local vertical system. Points on the lower, middle and upper level of racks were measured for good and correct interpretation of results. Testing the measured positional change of poles is on the end of this paper. The immediate readiness of interest groups of subjects for adopting necessary actions to ensure the stability and safe operation of the whole network of lanes of the warehouse spaces is the expected contribution of the presented results.

Statistical analysis is helpful for better understanding of the processes which take place in agricultural ecosystems. Particular attention should be paid to the processes of crops’ productivity formation under the influence of natural and anthropogenic factors. The goal of our study was to provide new theoretical knowledge about the dependence of vegetable crops’ productivity on water supply and heat income. The study was conducted in the irrigated conditions of the semi-arid cold Steppe zone on the fields of the Institute of Irrigated Agriculture of NAAS, Kherson, Ukraine. We studied the historical data of productivity of three most common in the region vegetable crops: potato, tomato, onion. The crops were cultivated by using the generally accepted in the region agrotechnology. Historical yielding and meteorological data of the period 1990–2016 were used to develop the models of the vegetable crops’ productivity. We used two approaches: development of pair linear models in three categories (“yield – water use”, “yield – sum of the effective air temperatures above 10°C”); development of complex linear regression models taking into account such factors as total water use, and temperature regime during the crops’ vegetation. Pair linear models of the crops’ productivity showed that the highest effect on the yields of potato and onion has the water use index (R2 of 0.9350 and 0.9689, respectively), and on the yield of tomato – temperature regime (R2 of 0.9573). The results of pair analysis were proved by the multiple regression analysis that revealed the same tendencies in the crop yield formation depending on the studied factors.