ISTRAŽIVANJE OPERACIJA MINIRANJA KORISTEĆI METODU ODLUČIVANJA

Blasting is one of the most important operations in the mining projects. Inappropriate blasting pattern may lead to unwanted events such as poor fragmentation, back break, fly rock etc. and affect the whole operation physically and economically. In fact selecting of the most suitable pattern among previously performed patterns can be considered as a Multi Attribute Decision MakingMiniranje je jedna od najvažnijih operacija pri projektiranju u rudarstvu. Nedovoljno dobar uzorak može pridonijeti nastanku neželjenih događaja kao što su fragmentizacija, pucanje, \u27fly rock\u27 itd. te utjecati na razvoj cijele operacije fizički i ekonomski. U principu, odabir najboljeg uzorka može se smatrati važnom odlukom

Metal-organig framework MIL-68(In)-NH2 on the membrane test bench for dye removal and carbon capture

The metal-organic framework (MOF) MIL-68(In)-NH2 was tested for dye removal from wastewater and carbon capture gas separation. MIL-68(In)-NH2 was synthesized as a neat, supported MOF thin film membrane and as spherical particles using pyridine as a modulator to shape the morphology. The neat MIL-68(In)-NH2 membranes were employed for dye removal in cross-flow geometry, demonstrating strong molecular sieving. MIL-68(In)-NH2 particles were used for electrospinning of poylethersulfone mixed-matrix membranes, applied in dead-end filtration with unprecedented adsorption values. Additionally, the neat MOF membranes were used for H2/CO2 and CO2/CH4 separation

CAR-T cell. the long and winding road to solid tumors

Adoptive cell therapy of solid tumors with reprogrammed T cells can be considered the "next generation" of cancer hallmarks. CAR-T cells fail to be as effective as in liquid tumors for the inability to reach and survive in the microenvironment surrounding the neoplastic foci. The intricate net of cross-interactions occurring between tumor components, stromal and immune cells leads to an ineffective anergic status favoring the evasion from the host's defenses. Our goal is hereby to trace the road imposed by solid tumors to CAR-T cells, highlighting pitfalls and strategies to be developed and refined to possibly overcome these hurdles

On zr-Ideals of C(X)

In this paper we introduce and study a class of ideals between z-ideals and z◦-ideals (=d-ideals) namely zr-ideals. A zr-ideal is a z-ideal which is at the same time an r-ideal (an ideal I in a ring R is called an r-ideal if for each non-zerodivisor r ∈ R and each a ∈ R, ra ∈ I implies a ∈ I). In contrast to the sum of z-ideals in C(X) which is a z-ideal, the sum of zr-ideals need not be a zr-ideal. We prove that the sum of every two zr-ideals of C(X) is a zr-ideal if and only if X is a quasi F-space. In C(X) every z◦-ideal is a zr-ideal and we characterize the spacesX for which the converse is also true. We observe that X is a cozero complemented space if and only if every (prime) r-ideal in C(X) is a z-ideal and whenever every (prime) z-ideal of C(X) is an r-ideal it is equivalent to X being an almost P-space.Using these facts it turns out that the set of all r-ideals and the set of all z-ideals of C(X) coincide if and only if X is a P-space

Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique

This research was aimed at developing a new model to predict flyrock distance based on a genetic programming (GP) technique. For this purpose, six granite quarry mines in the Johor area of Malaysia were investigated, for which various controllable blasting parameters were recorded. A total of 262 datasets consisting of six variables (i.e., powder factor, stemming length, burden-to-spacing ratio, blast-hole diameter, maximum charge per delay, and blast-hole depth) were collected applied to developing the flyrock predictive model. To identify the optimum model, several GP models were developed to predict flyrock. In the same way, using non-linear multiple regression (NLMR) analysis, various models were established to predict flyrock. Finally, to compare the performance of the developed models, regression coefficient (R2), root mean square error (RMSE), variance account for (VAF), and simple ranking methods were computed. According to the results obtained from the test dataset, the best flyrock predictive model was found to be the GP based model, with R2Â =Â 0.908, RMSEÂ =Â 17.638 and VAFÂ =Â 89.917, while the corresponding values for R2, RMSE and VAF for the NLMR model were 0.816, 26.194, and 81.041, respectively

A NOVEL INVESTIGATION IN BLASTING OPERATION MANAGEMENT USING DECISION MAKING METHODS

Blasting is one of the most important operations in the mining projects. Inappropriate blasting pattern may lead to unwanted events such as poor fragmentation, back break, fly rock etc. and affect the whole operation physically and economically. In fact selecting of the most suitable pattern among previously performed patterns can be considered as a Multi Attribute Decision Making

Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation

In addition to all benefits of blasting in mining and civil engineering applications, it has some undesirable environmental impacts. Backbreak is an unwanted phenomenon of blasting which can cause instability of mine walls, decreasing efficiency of drilling, falling down of machinery, etc. Recently, the use of new approaches such as artificial intelligence (AI) is greatly recommended by many researchers. In this paper, a new AI technique namely genetic programing (GP) was developed to predict BB. To prepare a sufficient database, 175 blasting works were investigated in Sungun copper mine, Iran. In these operations, the most influential parameters on BB including burden, spacing, stemming length, powder factor and stiffness ratio were measured and used to develop BB predictive models. To demonstrate capability of GP technique, a non-linear multiple regression (NLMR) model was also employed for prediction of BB. Value account for (VAF), root mean square error (RMSE) and coefficient of determination (R2) were used to control the capacity performance of the predictive models. The performance indices obtained by GP approach indicate the higher reliability of GP compared to NLMR model. RMSE and VAF values of 0.327 and 97.655, respectively, for testing datasets of GP approach reveal the superiority of this model in predicting BB, while these values were obtained as 0.865 and 81.816, respectively, for NLMR model