Investigation of influence of constraints with radius links on locomotive axle load distribution and wheelset steering ability

The problem of improving the tractive and dynamic performance of locomotives by means of using axle boxes with inclined radius links was investigated. The kinematics of axle boxes with different variants (different angles) and designs of inclined radius links, based on the vector contour method for link mechanisms, was studied. The results of a bogie design with vertically inclined radius links were used for the improvement of vertical load distribution for locomotives. The behavior of a locomotive running in a curve is more complicated because the lateral forces appear between a flange of the wheel and the head of the rail in the contact zone, and the values of wheel slip are higher than for straight sections of track. These two factors are the main reasons for wheel wear. Theoretical and experimental investigations to reduce wear and to improve vehicle dynamics were made, and the results obtained show that an effective solution can be reached by the use of inclined links to the longitudinal axis of a bogie in the horizontal plane. Questions about the development of a bogie with a radial installation and active steering are discussed.     

Surface properties of porous carbons obtained from polystyrene-based polymers within inorganic templates: role of polymer chemistry and inorganic template pore structure

Three inorganic adsorbents were applied as templates to produce porous carbons from polystyrene-based organic polymers. As matrices, amorphous silica gel, mesoporous alumina and microporous zeolite 13X were used. Organic precursors were polystyrene sulfonic acid (co-maleic acid) sodium salt and polystyrene co-maleic acid isobutyl/methyl mixed ester. The impregnated templates were carbonized at 800 °C. After removal of inorganic matrices porous carbons were obtained. Materials were characterized by adsorption of nitrogen, thermal analysis, potentiometric titration and SEM. Owing to the template carbonization, highly mesoporous carbons were obtained (S_BET up to 1500 m²/g, V_t up to 3 cm³/g) with majority of pores with sizes between 20–200 Å. Although the carbons were not replicas of their matrices, the carbonization within the confined space with utilization of self-released pore formers resulted in unique carbonaceous materials with very acidic surface. That acidity is linked to either exothermic effect of sodium reactivity with moist air or susceptibility for air oxidation of small graphene layers formed in the confined pore space.     

Effect of Al$$_$$O$$_3$$3 Nanoparticles Additives on the Density,Saturated Vapor Pressure,Surface Tension and Viscosity of Isopropyl Alcohol

This paper presents results of an experimental study of the density, saturated vapor pressure, surface tension and viscosity of Al\(_2\)O\(_3\) nanoparticle colloidal solutions in isopropyl alcohol. Studies of the thermophysical properties of nanofluids were performed at various temperatures and concentrations of Al\(_2\)O\(_3\) nanoparticles. The paper gives considerable attention to a turbidimetric analysis of the stability of nanofluid samples. Samples of nanofluids remained stable over the range of parameters of the experiments, ensuring the reliability of the thermophysical property data for the Al\(_2\)O\(_3\) nanoparticle colloidal solutions in isopropyl alcohol. The studies show that the addition of Al\(_2\)O\(_3\) nanoparticles leads to an increase of the density, saturated vapor pressure and viscosity, as well as a decrease for the surface tension of isopropyl alcohol. The information reported in this paper on the various thermophysical properties for the isopropyl alcohol/Al\(_2\)O\(_3\) nanoparticle model system is useful for the development of thermodynamically consistent models for predicting properties of nanofluids and correct modeling of the heat exchange processes.     

ReliefF-MI: An extension of ReliefF to multiple instance learning

In machine learning the so-called curse of dimensionality, pertinent to many classification algorithms, denotes the drastic increase in computational complexity and classification error with data having a great number of dimensions. In this context, feature selection techniques try to reduce dimensionality finding a new more compact representation of instances selecting the most informative features and removing redundant, irrelevant, and/or noisy features. In this paper, we propose a filter-based feature selection method for working in the multiple-instance learning scenario called ReliefF-MI; it is based on the principles of the well-known ReliefF algorithm. Different extensions are designed and implemented and their performance checked in multiple instance learning. ReliefF-MI is applied as a pre-processing step that is completely independent from the multi-instance classifier learning process and therefore is more efficient and generic than wrapper approaches proposed in this area. Experimental results on five benchmark real-world data sets and 17 classification algorithms confirm the utility and efficiency of this method, both statistically and from the point of view of execution time.     

Mössbauer spectroscopic studies of the mineralogical changes in coal as a function of cleaning,pyrolysis, combustion and coalconversion processes

The mineralogical changes in a Perry County, Illinois coal from the Herrin (No. 6) Member due to cleaning, pyrolysis, combustion, and coal-conversion processes were studied. Mössbauer spectroscopy was used in tandem with X-ray diffraction to follow the changes in the forms of iron originally present in the coal resulting from processing. The chemistry of the pyrite conversion is less complex than expected. Iron does not become uniformly distributed in all possible minerals but tends to form simple products. Pyrrhotites along with spinel and hydrated ferrous sulphates are the primary mineral products found in coat liquefaction and pyrolysis process residues; while mullite, ferrous silicates and the iron oxides (hematite, geothite and magnetite) are the most abundant mineral products found in Lurgi gasification and power plant fly ashes. The detailed distribution of iron, however, is dependent upon conditions in the particular process equipment in which the coal is used and the conversion process in which it is used.     

Adsorption of hydrogen sulfide on graphite derived materials modified by incorporation of nitrogen

Adsorbents for hydrogen sulfide were prepared by heat treatment at 950 °C of spent graphite oxide based materials previously used as ammonia removal media. The materials were characterized using adsorption of nitrogen, potentiometric titration, thermal analysis, FTIR and XRD. The dynamic adsorption experiments showed an enhanced adsorption capacity owing to the presence of basic nitrogen functionalities. Although on the surface of adsorbents hydrogen sulfide was mainly oxidized to elemental sulfur small amount of SO₂ was also formed. The removal capacity was found to depend on the dispersion of nitrogen containing species, which is governed by the porosity and structure of the adsorbents. The amount of nitrogen incorporated depends on the amount of ammonia on the surface and on the surface reactivity. The latter is determined by the amount of oxygen containing groups and the level of structural heterogeneity.     

Role of graphite precursor in the performance of graphite oxides as ammonia adsorbents

Graphite oxide was synthesized from three commercial graphites. The samples were used as adsorbents of ammonia at dry and wet conditions. Exceptionally high capacities reaching 10 wt% were measured. The initial and exhausted materials were characterized using adsorption of nitrogen, XRD, FTIR, TA, and potentiometric titration. The results showed differences in the texture and chemistry of adsorbents depending on the type of graphite precursor whose features determine the mechanism of ammonia adsorption. The strongly adsorbed species are either interacting with carboxylic groups to form salts, or with epoxy groups to form amines. A small amount of ammonia is weakly adsorbed via dispersive interactions or dissolved in the film of adsorbed water. As the concentration of amorphous carbon in the initial graphite increases, the ammonia removal capacity on the derived graphite oxide is improved.     

Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?

Sales prediction is an essential part of stock planning for the wholesales and retail business. It is a complex task because of the large number of factors affecting the demand. Designing an intelligent predictor that would beat a simple moving average baseline across a number of products appears to be a non-trivial task. We present an intelligent two level sales prediction approach that switches the predictors depending on the properties of the historical sales. First, we learn how to categorize the sales time series into ‘predictable’ and ‘random’ based on structural, shape and relational features related to the products and the environment using meta learning approach. We introduce a set of novel meta features to capture behavior, shape and relational properties of the sales time series. Next, for the products identified as ‘predictable’ we apply an intelligent base predictor, while for ‘random’ we use a moving average. Using the real data from a food wholesales company we show how the prediction accuracy can be improved using this strategy, as compared to the baseline predictor as well as an ensemble of predictors. In our study we also show that by applying an intelligent predictor for the most ‘predictable’ products we can control the risk of performing worse than the baseline.