The bipartite edge frustration of graphs under subdivided edges and their related sums

The bipartite edge frustration of a graph G, denoted by φ(G), is the smallest number of edges that have to be deleted from G to obtain a bipartite spanning subgraph of G. This topological index is related to the well-known Max-cut problem, and has important applications in computing stability of fullerenes. In this paper, the bipartite edge frustration of an infinite family of fullerenes is computed. Moreover, this quantity for four classes of graphs arising from a given graph under different types of edge subdivisions is investigated.     

Development of broadband forward‐wave directional couplers loaded by periodic patterned ground structure

In this paper two wideband Forward‐Wave Directional Couplers (FWDCs) with 0 dB and 3 dB coupling level are proposed. Using periodic patterned ground structure in a microstrip coupled lines by a new unit cell; even‐ and odd‐mode characteristic impedances of the couplers are equal over a wide frequency range. Moreover, it provides a constant phase difference between even and odd‐modes. The proposed cell is modeled using the equivalent circuit model and a design procedure is introduced for designing FWDCs for an arbitrary value of coupling level. The introduced couplers are numerically investigated and a prototype of both couplers is made. It is shown that for 0 dB coupling level, the measured coupling is 0.85 dB with 1 dB flatness over fractional bandwidth of 96% bandwidth. In case of 3 dB coupling, the measured coupling level is 3.5 dB at 7.42 GHz with 1 dB flatness over fractional bandwidth of 67.1%.     

Evolving Fuzzy Min–Max Neural Network Based Decision Trees for Data Stream Classification

Learning from data streams is a challenging task which demands a learning algorithm with several high quality features. In addition to space complexity and speed requirements needed for processing the huge volume of data which arrives at high speed, the learning algorithm must have a good balance between stability and plasticity. This paper presents a new approach to induce incremental decision trees on streaming data. In this approach, the internal nodes contain trainable split tests. In contrast with traditional decision trees in which a single attribute is selected as the split test, each internal node of the proposed approach contains a trainable function based on multiple attributes, which not only provides the flexibility needed in the stream context, but also improves stability. Based on this approach, we propose evolving fuzzy min–max decision tree (EFMMDT) learning algorithm in which each internal node of the decision tree contains an evolving fuzzy min–max neural network. EFMMDT splits the instance space non-linearly based on multiple attributes which results in much smaller and shallower decision trees. The extensive experiments reveal that the proposed algorithm achieves much better precision in comparison with the state-of-the-art decision tree learning algorithms on the benchmark data streams, especially in the presence of concept drift.     

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.     

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.     

Modelling useful energy demand system as derived from basic needs in the household sector

Inter-fuel substitution in the household sector depends on whether their target energy use is similar or not. To account for the effect of end-use application on energy demand, the concept of useful energy is utilized in which energy carriers are grouped according to their end-use applications. Useful energy is assumed as a commodity demanded to satisfy needs. Therefore, it should possess certain characteristics in accordance with the nature of basic needs. These characteristics were investigated through a two-level budgeting system with demographic variables indicating rural and urban households of Iran. The model has been applied to estimate the behavioural parameters such as income and price elasticities of useful energy demand. The estimated values of income and own-price elasticity show that all categories of useful energy are necessities with a relatively inelastic demand. Among them, cooling and non-substitutable electricity has the highest income and own-price elasticity, while lighting and water heating are ranked as the most necessary types of useful energy due to their low-income elasticity. In addition, small values of cross price elasticities support the idea that different types of useful energy are almost independent. Therefore, the results confirm that useful energy demands could be considered as basic needs.     

Improved physical stability of docosahexaenoic acid and eicosapentaenoic acid encapsulated using nanoliposome containing α‐tocopherol

This study aimed to develop a nanoliposomal formulation containing α‐tocopherol loaded with eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) and to characterise the formulation by its physical stability. For this purpose, different nanoliposomal formulations with dipalmitoyl phosphocholine were prepared using a modified thin‐film hydration method and evaluated by particle size, polydispersity index (PDI), transmission electron microscopy, differential scanning calorimetry and determining the encapsulation efficiencies of DHA and EPA. A physical stability study was conducted by investigating the change in the vesicle encapsulation efficiency, particle size, PDI and shape when stored at 4, 30 and 40 °C for 3 months. High encapsulation efficiency of DHA and EPA (89.1% ± 0.6% and 81.9% ± 1.4%) and appropriate particle size (82 ± 0.8 nm) were obtained for liposomes composed of α‐tocopherol. The optimum formulation was stable for 90 days when kept at 4 °C. This study demonstrated that α‐tocopherol had a protective effect on the physical stability of the nanoliposomes containing DHA and EPA.