Optimal ABC inventory classification using interval programming

Inventory classification is one of the most important activities in inventory management, whereby inventories are classified into three or more classes. Several inventory classifications have been proposed in the literature, almost all of which have two main shortcomings in common. That is, the previous methods mainly rely on an expert opinion to derive the importance of the classification criteria which results in subjective classification, and they need precise item parameters before implementing the classification. While the problem has been predominantly considered as a multi-criteria, we examine the problem from a different perspective, proposing a novel optimisation model for ABC inventory classification in the form of an interval programming problem. The proposed interval programming model has two important features compared to the existing methods: it provides optimal results instead of an expert-based classification and it does not require precise values of item parameters, which are not almost always available before classification. Finally, by illustrating the proposed classification model in the form of numerical example, conclusion and suggestions for future works are presented.     

Annual Rainfall Forecasting Using Hybrid Artificial Intelligence Model: Integration of Multilayer Perceptron with Whale Optimization Algorithm

Water Resources Management - Rainfall, as one of the key components of hydrological cycle, plays an undeniable role for accurate modelling of other hydrological components. Therefore, a precise...     

Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)

In this study, diagnosis of hepatitis disease, which is a very common and important disease, is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM) and simulated annealing (SA). Simulated annealing is a stochastic method currently in wide use for difficult optimization problems. Intensively explored support vector machine due to its several unique advantages is successfully verified as a predicting method in recent years. We take the dataset used in our study from the UCI machine learning database. The classification accuracy is obtained via 10-fold cross validation. The obtained classification accuracy of our method is 96.25% and it is very promising with regard to the other classification methods in the literature for this problem.     

Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques

There is growing demand on datacenters to serve more clients with reasonable response times, demanding more hardware resources, and higher energy consumption. Energy-aware datacenters have thus been amongst the forerunners to deploy virtualization technology to multiplex their physical machines (PMs) to as many virtual machines (VMs) as possible in order to utilize their hardware resources more effectively and save power. The achievement of this objective strongly depends on how smart VMs are consolidated. In this paper, we show that blind consolidation of VMs not only does not reduce the power consumption of datacenters but it can lead to energy wastage. We present four models, namely the target system model, the application model, the energy model, and the migration model, to identify the performance interferences between processor and disk utilizations and the costs of migrating VMs. We also present a consolidation fitness metric to evaluate the merit of consolidating a number of known VMs on a PM based on the processing and storage workloads of VMs. We then propose an energy-aware scheduling algorithm using a set of objective functions in terms of this consolidation fitness metric and presented power and migration models. The proposed scheduling algorithm assigns a set of VMs to a set of PMs in a way to minimize the total power consumption of PMs in the whole datacenter. Empirical results show nearly 24.9% power savings and nearly 1.2% performance degradation when the proposed scheduling algorithm is used compared to when other scheduling algorithms are used.     

Setting Up High-Throughput Low-Volume Sequencing and PCR Reactions Using an Automated System Equipped with Precision Glass Syringes and a Non-Contact Microsolenoid Dispenser

The advantages of using a new automated system, the Hydra-Plus-One System equipped with 96 or 384 precision glass syringes and a non-contact microsolenoid dispenser, in setting up high-throughput low-volume sequencing reactions and PCR are described. Using the syringe-based dispenser, which is the Hydra-PP part of this system, wet dispenses of as small as 100 nL with CVs of less than 10% can be accomplished. The single-channel, non-contact microsolenoid dispenser part of the system can dispense samples as low as 100 nL (with CVs of less than 10%) at a speed of 58s per 96 dispenses into any plate format (SBS footprint). The advantages associated with the use of the Hydra-Plus-One System for setting up PCR and sequencing reactions are high precision at nanoliter-dispense range; speed; and minimal waste of precious and expensive samples. The single-channel dispenser eliminates the dead volume associated with aspirating from reservoirs or troughs and thereby reduces sample waste. In addition, virtually all material can be recovered from the dispenser. Finally, non-contact dispensing enables distribution of sample into wells without any inbetween-dispenses washing requirements.     

Characterization of a new shielding rubber for use in neutron–gamma mixed fields

A variety of formulations was investigated for the fabrication of an appropriate shielding rubber to be used in neutron–gamma mixed fields. Having considered the required mechanical properties together with tungsten as the gamma-ray absorbing element, calculations with MCNPX 2.6 code confirmed that the incorporation of 5 weight percentage(wt%) of boron carbide exhibited the best performance as a thermal neutron absorber. A series of both experimental and simulation results are provided for comparison.     

Kinetic studies on extra heavy crude oil upgrading using nanocatalysts by applying CFD techniques

Regarding the growth of global energy consumption and the paucity of light crude oil,extracting and using heavy and extra heavy crude oil has received much more attention,but the application of this kind ofoil is complicated due to its very high molecular weight.High viscosity and low flowability complicate the transportation of heavy and extra heavy crude oil.Accordingly,it is essential to reduce the viscosity of heavy and extra heavy crude oil through in-situ operations or immediate actions after extraction to reduce costs.Numerical simulations are influential methods,because they reduce calculation time and costs.In this study,the cracking of extra heavy crude oil using computational fluid dynamics is simulated,and a unique kinetic model is proposed based on experimental procedures to predict the behavior of extra heavy crude oil cracking reaction.Moreover,the hydrodynamics and heat transfer of the system and influence of nanocatalysts and temperature on the upgrading of crude oil are studied.The geometry of a reactor is produced using commercial software,and some experiments are performed to examine the validity and accuracy of the numerical results.The findings reveal that there is a good agreement between the numerical and experimental results.Furthermore,to investigate the main factors affecting the process,sensitivity analysis is adopted.Results show that type of catalyst and concentration of catalyst are the parameters that influence the viscosity reduction of extra heavy crude oil the most.The findings further revealed that when using a 25 nm SiO2 nanocatalyst,a maximum viscosity reduction of 98.67％ is observed at 623 K.Also,a catalyst concentration of 2.28wt％ is best for upgrading extra heavy crude oil.The results obtained through sensitivity analysis,simulation model,and experiments represent effectual information for the design and development of high performance upgrading processes for energy applications.     

Enrichment of omega3 fatty acids from Tyulka oil by supercritical CO2 extraction

BACKGROUND: Supercritical CO₂ enrichment of omega3 essential fatty acids (FAs) from Tyulka oil, using a batch process was investigated. Fractional factorial design was applied to evaluate the effects of the five process parameters: pressure (20.26 to 25.33 MPa); temperature (40 to 50 °C); packing fraction (0.5 to 0.7); modifier fraction (2 to 5%); and dynamic time (15 to 25 min), and their binary interactions on the enrichment of extracted omega3 FAs. By employing experimental design and analysis of variance, the variables were evaluated according to the significance of their effect on the yield of extracted omega3. RESULTS: The experimental results confirmed that pressure and dynamic time were the most important factors affecting enrichment of omega3. The amount of modifier in the feed also showed an increasing effect on the response. The binary interaction effects were investigated, and are discussed in detail. CONCLUSION: Optimum conditions were found at 25.33 MPa, 46.65 °C, packing fraction 0.50, modifier 5% and dynamic time 25 min, improving the enrichment of omega3 FAs up to 2.9 times. Copyright © 2009 Society of Chemical Industry     

Three-dimensional FE analysis of ring rolling by employing thermal spokes method

This paper investigates the performance of the “Thermal Spokes Method” in modeling the effect of the guide rolls on the ring rolling process. If the guide rolls are not included FE, simulation encounters problems such as tilting of the ring and process instability. Thermal spokes are introduced to withstand any unwanted moment of the resultant rolling forces at the roll gap and to maintain the ring in a centralized location in the mill. By employing the thermal spokes method, much closer predictions for the lateral spread and flow patterns are calculated. This method is able to predict the tilting of the ring with respect to the stiffness of the elements of the adjustment mechanism. It is also possible by this method to predict the point at which the ring changes its support from one guide roll to another one and to study the deformation mechanism outside the roll gap. To verify the validity of the proposed method, the predicted results from this method are compared with some available references. Since the proposed method considers the effect of the guide rolls and consequently offers much closer predictions, it is a very suitable method for ring rolling simulations.     

Neck,shoulder and low back pain in secondary schoolchildren in relation to schoolbag carriage: Should the recommended weight limits be gender-specific?

The occurrence of neck, shoulder and low back complaints in relation to schoolbag carriage and other potential risk factors were investigated in a cross-sectional study of 586 Iranian schoolchildren aged 12–14 years. The average load carried by schoolchildren was 2.8 kg. Neck, shoulder and low back complaints during the preceding month were reported by 35.3%, 26.1% and 33% of the students, respectively. Gender was an independent factor predicting musculoskeletal symptoms in schoolchildren. Girls were more likely than boys to suffer from neck, shoulder and low back complaints, although there was no significant difference between genders in terms of schoolbag carriage variables. The findings suggest that the recommended weight limit for schoolbag carriage may need to differ between boys and girls. The associations between schoolbag variables and reported symptoms are also discussed. The results provide evidence that the current weight limit should consider a broader combination of factors that influence the use of schoolbags.