A new MILP model proposal in feed formulation and using a hybrid-linear binary PSO (H-LBP) approach for alternative solutions

Large-scale feed factories may have multiple production and storage facilities. Any production facility uses its own available raw materials while performing feed formulation. However, ensuring a reasonable cost is achieved, and the desired quality criteria are met, may require obtaining a certain amount of raw material from other facilities. Selecting a specific amount of raw materials among many raw materials in different facilities requires many combinations to be tried out. Providing solutions, especially when there is a large amount of the raw material, may be costly and take more time. A new mixed-integer linear programming (MILP) model that specifies the type of material and the amount of the material to be selected from external facilities has been proposed in this study. When deterministic methods like MILP are used, only one solution result is obtained. However, when the decision-maker would like to see alternative results, solution constraints can be mitigated and a solution provided within the same or similar time. A new method named hybrid-linear binary PSO (H-LBP) has been proposed in this study for the problems that the decision-maker had limited time for and for which the solution results were required in a shorter time. Continuous particle swarm optimization, which works as a hybrid with linear programming, has been used in this method. The new model proposed in this study was tested on the mixed feeds for sheep, cattle and rabbit species by using both MILP and the proposed H-LBP methods. Raw materials determined by the model were added to the mixture, and the cost in each of the three species was observed to go down. In addition, different alternative solutions at reasonable cost and similar quality were presented to the producer/decision-maker in a shorter time.

     

Enhancement of (Bi,Pb)-2223 HTS Formation by Boron-Doping

Influence of boron-doping on the superconducting properties of (Bi,Pb)-2223 HTS ceramics prepared in an alumina crucibles has been investigated. X-ray diffraction, resistivity, and AC susceptibility measurements were performed on the undoped and boron-doped compounds. Obtained results have shown that B₂O₃ addition in the Bi_1.7Pb_0.3Ca₂Sr₂Cu₃O _y precursor enhances the formation of high-T _c phase. The boron-doped samples with starting composition Bi_1.7Pb_0.3Ca₂Sr₂Cu₃B _x O _y (x=0.05 and 0.5) reveal significant improvement in the zero resistivity temperature compared to the undoped sample (from 72 K up to 100 K). Boron-doping level x=1.5 results in a substantial degradation of the (Bi,Pb)-2223 phase.     

Recent progress in Co-based metal-organic framework derivatives for advanced batteries

To date,Co-based metal-organic frameworks (Co-MOFs) have drawn much attention owing to their ad-vantages of easy preparation,high porosity and adjustable struct...     

A robust technique based on invariant moments – ANFIS for recognition of human parasite eggs in microscopic images

In this study, we propose a robust technique based on invariant moments – adaptive network based fuzzy inference system (IM-ANFIS). In this technique, some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of IM-ANFIS approach used in this study. Recently, the pattern recognition principles have come into prominence. The pattern recognition includes operation and design of systems that recognize patterns in data sets. Important application areas of pattern recognition techniques are character recognition, speech analysis, image segmentation, man and machine diagnostics and industrial inspection. The technique presented in this study enables to classify 16 different parasite eggs from their microscopic images. This proposed recognition method includes three stages. In first stage, a preprocessing subsystem is realized for obtaining unique features from the same group of patterns. In second stage, a feature extraction mechanism which is based on the invariant moments is used. In third stage, an adaptive network based fuzzy inference system (ANFIS) classifier is used for recognition process. We conduct computer simulations on MATLAB environment. The overall success rate is almost 95%.     

Modelling and analysis of vehicle-structure-road coupled interaction considering structural flexibility,vehicle parameters and road roughness

To determine the dynamic forces acting on vehicle components a vehicle-structure-road interaction is considered. Coupled interaction was modelled using a flexible bridge-like thin beam structure with four and six Degree of freedom (DOF) half car models. In addition to the flexible structure, the road conditions were added to the model as random and non-random surface irregularities. A coupled equation of motion of the whole system was derived using Lagrange equations, and converted to a first-order state-space equation and then solved using the fourth-order Runge-Kutta method. Besides the dynamic forces, the effects of the vehicle speed, bridge flexibility, tire stiffness, random or non-random road irregularities on the passenger comfort are widely investigated. The results obtained were compared by several early VBI (Vehicle-bridge-interaction) studies in the literature and proved accurate with a 5 % difference.