Modelling of the drying of eggplants in thin-layers

In this paper, the thin‐layer drying behaviour of eggplant slices (6 mm thick layers) in a convective‐type cyclone dryer is reported. Thin‐layer drying experiments were conducted at drying air temperatures of 55, 65 and 75 °C and dry air velocities of 1 and 1.5 ms^?1. Data on sample mass, temperature and velocity of the dry air were recorded continuously during each test. In order to estimate and select a suitable form of the drying curve, eight different semi‐theoretical and/or empirical models were fitted to the experimental data and comparisons made of their coefficients of determination as predicted by non‐linear regression analysis. The Page model best described the drying curve of eggplant, with a correlation coefficient, r = 0.9999.     

Adaptive neuro-fuzzy computing technique for suspended sediment estimation

This paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in suspended sediment estimation. The monthly streamflow and suspended sediment data from two stations, Kuylus and Salur Koprusu, in Kizilirmak Basin in Turkey are used as case studies. The estimation results obtained by using the neuro-fuzzy technique are tested and compared with those of the artificial neural networks and sediment rating curves. Root mean squared errors, mean absolute errors and correlation coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neuro-fuzzy models can be employed successfully in monthly suspended sediment estimation.     

Gain gradients applied to optimization of distributed‐parameter matching circuits for a microwave transistor subject to its potential performance

In this article, a rigorous design procedure is carried out for a microwave amplifier by employing the Feasible Design Space and simple analytical gain gradients of the matching circuits. Physical lengths and characteristic impedances of the transmission lines used in the matching circuits are chosen as the design variables and their lower and upper limits are bounded by the limits of the planar transmission line technology so that resulted microwave amplifier can be realized by this technology. Feasible Design Target Space is determined by the compatible performance [noise (F), input VSWR (V_i), gain (G_T)] triplets and their source Z_S(ω_i) and load Z_L(ω_i) terminations resulted from the performance characterization of the active device. These triplets take into account the physical limitations of the device and realization conditions so that F_req ≥ F_min, V_ireq ≥ 1, G_{T min} ≤ G_{T req} ≤ G_{T max}; and Z_S(ω_i) and Z_L(ω_i) terminations be taken place within the “Unconditionally Stable Working Area”. Design of the amplifier for the compatible performance triplets is reduced to the design of the Z_S(ω_i) and Z_L(ω_i), i = 1…N terminations, which is achieved by the gain optimization of the two passive, reciprocal matching two‐ports using the Darlington theorem. Analytical expressions of the gain gradients of the matching circuits are obtained by the two different methods: (i) chain sensitivity matrix approach; (ii) adjoint network approach. Gain gradients of the L‐, T‐, and Π‐types of distributed‐parameter matching circuits are obtained as worked examples. Then typical design examples are given with together the synthesized, target, simulated characteristics. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.     

Comparison of acid and enzymatic hydrolysis of tobacco stalk xylan for preparation of xylooligosaccharides

Tobacco stalk (TS), a major agricultural waste in the Black Sea region of Turkey, was used for the production of xylooligosaccharides (XOs). It contains about 22 g/100 g xylan whose composition was determined as 93.5 g/100 g xylose, 6.54 g/100 g glucose and 11.2 g/100 g uronic acid after complete acid hydrolysis. XO production was performed by enzymatic and acid hydrolysis of xylan which was obtained by alkali extraction from tobacco stalk. In enzyme hydrolysis, xylan was hydrolyzed using a xylanase preparation and the effects of pH, temperature, hydrolysis period, substrate and enzyme concentrations on the xylooligosaccharide yield and degree of polymerization were investigated. For enzymatic hydrolysis under optimum conditions XO yield with respect to tobacco stalk xylan (TSX) was 8.2 g/100 g after 8 h and 11.4 g/100 g after 24 h reaction period. In the acid hydrolysis, sulfuric acid was used and the hydrolyzate contained different amount of oligosaccharides and monosaccharides. For acid hydrolysis under optimum conditions, XO yield with respect to TSX was 13.0 g/100 g. Enzymatically obtained oligosaccharides were purified via ultrafiltration by using 10 and 3 kDa membranes. After a two-step membrane processing, the permeate containing mostly oligosaccharides was obtained.     

Statistically monotonic and statistically bounded sequences of fuzzy numbers

We introduce the statistical monotonicity and boundedness of a sequence of fuzzy numbers. We also derive the analogue of monotone convergence theorem and prove the decomposition theorems for this type of sequences.     

Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing

This paper presents a novel method for diagnosis of heart disease. The proposed method is based on a hybrid method that uses fuzzy weighted pre-processing and artificial immune recognition system (AIRS). Artificial immune recognition system has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, liver disorders classification. The robustness of the proposed method is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 96.30% and it is very promising compared to the previously reported classification techniques.     

The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases

This paper presents a novel method for differential diagnosis of erythemato-squamous disease. The proposed method is based on fuzzy weighted pre-processing, k-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier. The proposed method consists of three parts. In the first part, we have used decision tree classifier to diagnosis erythemato-squamous disease. In the second part, first of all, fuzzy weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified using decision tree classifier. In the third part, k-NN based weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified via decision tree classifier. The employed decision tree classifier, fuzzy weighted pre-processing decision tree classifier, and k-NN based weighted pre-processing decision tree classifier have reached to 86.18, 97.57, and 99.00% classification accuracies using 20-fold cross validation, respectively.     

Osteogenic proteins (bone sialoprotein and bone morphogenetic protein-7) and dental pulp mineralization

Bone sialoprotein (BSP) cross-linked to collagen/gelatin was implanted in the pulp of rats upper molars. Comparison was carried out with a sham group (non implanted), with a group of rats receiving the carrier alone, and a group of molars where the perforated pulps were capped with calcium hydroxide. The cavities were occluded with a glass-ionomer cement (GIC). After 8, 14 and 30 days respectively the rats were killed by intracardiac perfusion of the fixative and processed for light microscopy. Dentin and predentin debris pushed into the pulp during the preparation enhanced self-repair processes, with large pulp remnants. The carrier alone induced slight inflammation, and calcium hydroxide the formation of a reparative dentin bridge. BSP stimulated the recruitment of cells which produced an homogeneous atubular dentin-like structure, filling after one month the mesial third of the crown pulp. Osteogenic protein (OP-1) used in the same experimental conditions induced the formation of osteodentin in the coronal pulp and the radicular part of the pulp was totally filled by a mineralized material. The differences reported here suggest two possible different therapeutic approaches with the two osteogenic proteins, BSP inducing pulp mineralization in the crown part, and OP-1 occluding the root part of the pulp.     

Improved Enhanced Dbtma with Contention-Aware Admission Control to Improve the Network Performance in Manets

DBTMA relies entirely on RTS/CTS dialogue for un-collided transmission of data. The purpose is to improve the QoS at MAC layer by developing it over 802.11e standard. However, DBTMA does not guarantee real-time constraints without efficient method for controlling the network loads. The main challenges in MANETs include prediction of the available bandwidth, establishing communication with neighboring nodes and predicting the consumption of bandwidth flow. These challenges are provided with solutions using Contention-Aware Admission Control (CACP) protocol. In this paper, the EDBTMA protocol is combined with CACP protocol that introduces bandwidth calculation using admission control strategy. The calculation includes certain metrics like: admission control and bandwidth consumption. To compute the bandwidth of channel, bandwidth utilization and traffic priority is distinguished through dual busy tone is proposed. This operates distinctly on its own packet transmission operation. This CACP mechanism defends the conventional traffic flows from new nodes and based on the measured status information of the channel, it QoS of the admitted flows is maintained. This ensures maximum amount of bandwidth flows accommodated by resources and determines the resources in a system meet the new flow requirements while maintaining existing bandwidth flow levels.     

Improving the discrimination power and weights dispersion in the data envelopment analysis

Data envelopment analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of peer decision making units (DMUs) with multiple input and outputs. Beside of its popularity, DEA has some drawbacks such as unrealistic input–output weights and lack of discrimination among efficient DMUs. In this study, two new models based on a multi-criteria data envelopment analysis (MCDEA) are developed to moderate the homogeneity of weights distribution by using goal programming (GP). These goal programming data envelopment analysis models, GPDEA-CCR and GPDEA-BCC, also improve the discrimination power of DEA.