Neural Models for the Resonant Frequency of Electrically Thin and Thick Circular Microstrip Antennas and the Characteristic Parameters of Asymmetric Coplanar Waveguides Backed with a Conductor

Neural models for computing the resonant frequency of electrically thin and thick circular microstrip antennas, based on the multilayered perceptrons and the radial basis function networks, are presented. Five learning algorithms, delta-bar-delta, extended delta-bar-delta, quick-propagation, directed random search and genetic algorithms, are used to train the multilayered perceptrons. The radial basis function network is trained according to its learning strategy. The resonant frequency results of neural models are in very good agreement with the experimental results available in the literature. In this paper, the characteristic impedance and the effective permittivity of the asymmetric coplanar waveguide backed with a conductor are also computed by using only one neural model trained by the backpropagation with momentum and the extended delta-bar-delta algorithms. When the performances of neural models are compared with each other, the best results for test are obtained from the multilayered perceptrons trained by the extended delta-bar-delta algorithm.     

Effect of Fe2+ and Fe0 Applied Photo‐Fenton Processes on Sludge Disintegration

The disintegration of waste active sludge was investigated by photo‐Fenton processes. A batch study was conducted to evaluate parameters, such as Fe²⁺ and Fe⁰ ions and H₂O₂, governing the activated sludge integration by the photo‐Fenton process. Under optimum conditions, the concentration of soluble chemical oxygen demand (SCOD) with the classical Fenton process (CFP) increased very rapidly in the first five minutes due to the sufficient presence of reaction components in the medium, and then the rate of increase declined. In the modified Fenton process (FTP), the SCOD concentration increased more slowly as metallic iron powder must first be dissolved. The photo‐Fenton process proved to be a feasible and efficient process for the disintegration of waste sludge.     

Solar-blind ultraviolet photodetector based on Ti-doped Ga2O3/Si p–n heterojunction

A solar-blind ultraviolet photodetector based on Ti-doped Ga₂O₃/Si p–n heterojunction is demonstrated for the first time. It is found that the heterojunction quality forming between Ga₂O₃ and Si becomes better after Ti incorporation in Ga₂O₃. The current–voltage and temporal response measurements show that the detector based on Ti-doped Ga₂O₃/Si p–n heterojunction has a responsivity of 0.382 A/W and a fast rise time of 73 ms as well, which are much better than those undoped Ga₂O₃/Si p–n heterojunction analogues.

     

Winners and losers of rapid growth in Turkey: Analysis of the spatial variability of convergence

This paper investigates spatial variability of regional convergence for the 2004-2017 period in Turkey. Results from neoclassical convergence model which are robust to inclusion of a spatial battery points out the existence of convergence. However, additional results highlight that regional convergence is spatially heterogeneous. While underdeveloped regions exhibit higher convergence, convergence speed is not distributed evenly among these underdeveloped eastern regions. These findings point out that, last epoch of Turkish transformation fails to have equity enhancing effects at local level, rather there are winners and losers of the rapid growth of 2000s.     

An integrated interval-valued intuitionistic fuzzy AHP-TOPSIS methodology to determine the safest route for cash in transit operations: a real case in Istanbul

Neural Computing and Applications - The Cash in Transit (CIT) deals with the money distribution and picking up between depot(s), central bank, bank branches, Automated Teller Machines (ATMs),...     

Functional and textural properties of vegetable‐fibre enriched yoghurt

In this study, different vegetable‐fibre enriched yoghurts namely control (C), pumpkin (PY), carrot (CY), green pea (GY) and zucchini (ZY), were produced. It was observed that Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus counts were higher in yoghurts supplemented with vegetable purees, in accordance with the enhanced growth of lactic acid bacteria due to the fibres, phenolic compounds and organic acids present in the vegetables. Furthermore, vegetable puree supplementation affected the pH, titratable acidity, syneresis, colour (L*, a*, b*, ΔE values), texture (firmness, cohesiveness, consistency and viscosity index) and sensorial properties of the yoghurts. Firmness, consistency and viscosity indices were higher in the yoghurt produced with carrot puree, whereas the highest antioxidant capacity was detected in the pumpkin yoghurt, which corresponded to the highest total phenolic, ascorbic acid and total carotenoid contents. The results of the present study could lead to an innovative approach in the functional dairy product market for the development of dairy products enriched with vegetables that have nutritional and potential therapeutic characteristics.     

Prediction of postpartum depression using machine learning techniques from social media text

Early screening of mental disorders plays a crucial role in diagnosis and treatment. This study explores how data‐driven methods can leverage the information available on social media platforms to predict postpartum depression (PPD). A generalized approach is proposed where linguistic features are extracted from user‐generated textual posts on social media and categorized as general, depressive, and PPD representative using multiple machine learning techniques. We find that techniques used in our study exhibit strong predictive capabilities for PPD content. Holdout validation showed that multilayer perceptron outperformed other techniques such as support vector machine and logistic regression used in this study with 91.7% accuracy for depressive content identification and up to 86.9% accuracy for PPD content prediction. This work adopts a hierarchical approach to predict PPD. Therefore, the reported PPD accuracy represents the performance of the model to correctly classify PPD content from non‐PPD depressive content.