An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank

Developing decision support system (DSS) can overcome the issues with personnel attributes and specifications. Personnel specifications have greatest impact on total efficiency. They can enhance total efficiency of critical personnel attributes. This study presents an intelligent integrated decision support system (DSS) for forecasting and optimization of complex personnel efficiency. DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. DEA has two roles in this study. It provides data to ANN and finally it selects the best reduct through ANN results. Reduct is described as a minimum subset of features, completely discriminating all objects in a data set. The reduct selection is achieved by RST. ANN has two roles in the integrated algorithm. ANN results are basis for selecting the best reduct and it is used for forecasting total efficiency. Finally, K-Means algorithm is used to develop the DSS. A procedure is proposed to develop the DSS with stated tools and completed rule base. The DSS could help managers to forecast and optimize efficiencies by selected attributes and grouping inferred efficiency. Also, it is an ideal tool for careful forecasting and planning. The proposed DSS is applied to an actual banking system and its superiorities and advantages are discussed.     

Lower limits of spin detection efficiency for two-parameter two-qubit (TPTQ) states with non-ideal ferromagnetic detectors

We have investigated a TPTQ state as an input state of a non-ideal ferromagnetic detectors. Minimal spin polarization required to demonstrate spin entanglement according to entanglement witness and CHSH inequality with respect to (w.r.t.) their two free parameters have been found, and we have numerically shown that the entanglement witness is less stringent than the direct tests of Bell’s inequality in the form of CHSH in the entangled limits of its free parameters. In addition, the lower limits of spin detection efficiency fulfilling secure cryptographic key against eavesdropping have been derived. Finally, we have considered TPTQ state as an output of spin decoherence channel and the region of ballistic transmission time w.r.t. spin relaxation time and spin dephasing time has been found.     

Mathematical Modeling and Genetic Algorithm Optimization of Reactive Absorption of H2S

A rate‐based mathematical model was developed for the reactive absorption of H₂S in NaOH, with NaOCl or H₂O₂ as the chemical oxidant solutions in a packed column. A modified mass transfer coefficient in the gas phase was obtained by genetic algorithm and implemented in the model to correct the assumption of instantaneous reactions. The effects of different operating variables including the inlet H₂S concentration, inlet gas mass flux, initial NaOH, concentrations of the chemical oxidants in the scrubbing solutions, and liquid‐to‐gas ratio on the H₂S removal efficiency were studied. A genetic algorithm was employed to optimize the operating variables in order to obtain maximum removal efficiency of H₂S. The model results were in good agreement with the experimental data.     

Improvement of the quality of gluten‐free sponge cake using different levels and particle sizes of carrot pomace powder

In this study, carrot pomace powder (CPP) with particle sizes of 210 μm (CPP210) and 500 μm (CPP500) was added in the gluten‐free sponge cake recipe. Flour (rice and corn flour, 1:1, w/w) was replaced with 0, 10, 20 and 30% CPP. With increasing the level and particle size of CPP, batter density, viscosity, consistency and firmness increased. The control cake had a dense, hard texture, irregular shape and low sensory scores. These properties improved with addition of CPP so that the cake density, hardness and cohesiveness reduced, while symmetry index and sensory scores increased. Varying the particle size of CPP had no considerable effects on most of the batter and cake properties, while increasing the level of CPP had great positive effects on the quality of batter and cake. Overall, addition of 30% CPP with either of the particle sizes resulted in an acceptable gluten‐free cake.     

Calcium: A comprehensive review on quantification,interaction with milk proteins and implications for processing of dairy products

Calcium (Ca) is a key micronutrient of high relevance for human nutrition that also influences the texture and taste of dairy products and their processability. In bovine milk, Ca is presented in several speciation forms, such as complexed with other milk components or free as ionic calcium while being distributed between colloidal and serum phases of milk. Partitioning of Ca between these phases is highly dynamic and influenced by factors, such as temperature, ionic strength, pH, and milk composition. Processing steps used during the manufacture of dairy products, such as preconditioning, concentration, acidification, salting, cooling, and heating, all contribute to modify Ca speciation and partition, thereby influencing product functionality, product yield, and fouling of equipment. This review aims to provide a comprehensive understanding of the influence of Ca partition on dairy products properties to support the development of kinetics models to reduce product losses and develop added-value products with improved functionality. To achieve this objective, approaches to separate milk phases, analytical approaches to determine Ca partition and speciation, the role of Ca on protein–protein interactions, and their influence on processing of dairy products are discussed.     

H2O based different nanofluids with unsteady condition and an external magnetic field on permeable channel heat transfer

This paper investigates numerically the problem of unsteady magnetohydrodynamic nanofluid flow and heat transfer between parallel plates due to the normal motion of the porous upper plate. The governing equations are solved via the fourth-order Runge-Kutta method. Different kind of nanoparticles is examined. The effects of kind of nanoparticle, nanofluid volume fraction, expansion ratio, Hartmann number, Reynolds number on velocity and temperature profiles are considered. Also effect of different types of nanoparticles is examined. Results indicate that velocity decreases with increase of Hartmann number due to effect of Lorentz forces. Rate of heat transfer increase with increase of nanofluid volume fraction, Hartmann number and Reynolds number but it decreases with increase of expansion ratio. Also it can be found that choosing copper as a nanoparticle leads to highest enhancement.