15 research outputs found
THE APPLICATION OF INTERVAL CALCULUS TO ESTIMATION OF PLATE DEFLECTION BY SOLVING POISSON’S PARTIAL DIFFERENTIAL EQUATION
Get PDFThis paper describes the application of interval calculus to calculation of plate deflection, taking in account inevitable and acceptable tolerance of input data (input parameters). The simply supported reinforced concrete plate was taken as an example. The plate was loaded by uniformly distributed loads. Several parameters that influence the plate deflection are given as certain closed intervals. Accordingly, the results are obtained as intervals so it was possible to follow the direct influence of a change of one or more input parameters on output (in our example, deflection) values by using one model and one computing procedure. The described procedure could be applied to any FEM calculation in order to keep calculation tolerances, ISO-tolerances, and production tolerances in close limits (admissible limits). The Wolfram Mathematica has been used as tool for interval calculation
Determination of Manufacturing Process Conditions by Using MCDM Methods: Application in Laser Cutting
No full textManufacturing is a primary generator of wealth of the country and is essential for economic growth. Determination of the most suitable manufacturing process conditions for a given application is very complex task and requires consideration of a number of conflicting and diverse process performance characteristics (criteria). In this paper the application of a recent multi-criteria decision making (MCDM) method, i.e. weighted aggregated sum product assessment (WASPAS) for determination of manufacturing process conditions in laser cutting was discussed. Laser cutting experiment was conducted based on Taguchi's L9 experimental design by varying the laser power, cutting speed, assist gas pressure and focus position at three levels. Based on obtained experimental results, a MCDM model consisting of nine alternatives and six criteria was defined. In order to determine the relative significance of considered criteria a pair-wise comparison matrix of the AHP method was used. Stability of the obtained ranking of alternatives was checked by varying values of coefficient of linear combination and by the application of operational competitiveness ratings analysis (OCRA) method
Combined ligand and fragment-based drug design of selective histone deacetylase – 6 inhibitors
No full textHistone deacetylase 6 (HDAC6) is unique hydrolase within HDAC family, having pleiotropic deacetylase activity against α-tubulin, cortactin and dynein. Comprehensively, HDAC6 controls cell motility, apoptosis and protein folding, whereas alterations in its structure and function are related to the pathogenesis of cancer, neurodegeneration and inflammation. To define structural motifs which guide HDAC6 selectivity, we developed and compared three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) models for HDAC1 and HDAC6 inhibitors. The reduction of the bias in conformer generation was supported by virtual docking study by using crystal structures of human HDAC1 and HDAC6 isoforms. Following these findings, the combined ligand-based and fragment-based drug design methodologies were used in the design of selective HDAC6 inhibitors. Group of the most promising novel ligands was selected based on the predicted HDAC6 selectivity, pharmacokinetic profile, synthetic tractability, and in silico cytotoxicity against the wide range of human cancer cell lines
Heat load prediction in district heating systems with adaptive neuro-fuzzy method
No full textDistrict heating systems can play significant role in achieving stringent targets for CO2 emissions with concurrent increase in fuel efficiency. However, there are a lot of the potentials for future improvement of their operation. One of the potential domains is control and prediction. Control of the most district heating systems is feed forward without any feedback from consumers. With reliable predictions of consumers heat need, production could be altered to match the real consumers' needs. This will have effect on lowering the distribution cost, heat losses and especially on lowered return secondary and primary temperature which will result in increase of overall efficiency of combined heat and power plants. In this paper, to predict the heat load for individual consumers in district heating systems, an adaptive neuro-fuzzy inferences system (ANFIS) was constructed. Simulation results indicate that further improvements on model are needed especially for prediction horizons greater than 1
