Some physicochemical properties and mineral contents of stirred yoghurts containing different fruit marmalades

In this study chemical properties and mineral levels of yoghurts containing different fruit marmalades were investigated. Cherry laurel, loquat fruit and kiwi marmalade were used to make the stirred yoghurts, and the calcium, phosphorus, sodium, magnesium, manganese, iron, zinc, copper, boron, lead, nickel, molybdenum, chromium, cadmium, cobalt levels were determined by an inductively coupled plasma optical emission spectrometer (ICP–OES). The total solid, fat, ash, pH, viscosity and syneresis contents of the yoghurt samples changed between 13.85–21.02%, 2.45–2.90%, 0.82–0.94%, 4.11–4.22, 2104–2454 cP and 42.0–47.0 mL/100 g, respectively. The mean concentrations in the yoghurts of these minerals were 104.66–118.94 calcium, 95.81–112.42 phosphorus and 13.47–15.04 magnesium as mg/100 g, 235.00–254.00 iron, 429.00–453.00 zinc and 70.45–122.90 copper as μg/100 g. The recommended dietary allowance (RDA) was calculated on the basis of the intake of one yoghurt portion (125 g).     

A fully automatic CAD-CTC system based on curvature analysis for standard and low-dose CT data

Computed tomography colonography (CTC) is a rapidly evolving noninvasive medical investigation that is viewed by radiologists as a potential screening technique for the detection of colorectal polyps. Due to the technical advances in CT system design, the volume of data required to be processed by radiologists has increased significantly, and as a consequence the manual analysis of this information has become an increasingly time consuming process whose results can be affected by inter- and intrauser variability. The aim of this paper is to detail the implementation of a fully integrated CAD-CTC system that is able to robustly identify the clinically significant polyps in the CT data. The CAD-CTC system described in this paper is a multistage implementation whose main system components are: 1) automatic colon segmentation; 2) candidate surface extraction; 3) feature extraction; and 4) classification. Our CAD-CTC system performs at 100% sensitivity for polyps larger than 10 mm, 92% sensitivity for polyps in the range 5 to 10 mm, and 57.14% sensitivity for polyps smaller than 5 mm with an average of 3.38 false positives per dataset. The developed system has been evaluated on synthetic and real patient CT data acquired with standard and low-dose radiation levels.     

A hybrid mobile object tracker based on the modified Cuckoo Search algorithm and the Kalman Filter

Most revolutionary algorithms are inspired from the behavior of natural species. This inspiration can be drawn from their reproductive behavior, flying mode, or even their ways of communication. One of the most efficient metaheuristics in a discrete search space is the Cuckoo Search algorithm, inspired by the Cuckoo?s reproductive behavior and combined with the Lévy flight pattern adopted by many animals and insects. In this paper, we present a new tracking approach, the Hybrid Kalman Cuckoo Search tracker, using a modified version of the Cuckoo Search algorithm combined with the well-known Kalman Filter. The Cuckoo Search algorithm is combined with the prediction step adopted by the Kalman Filter to enhance the initial population?s quality. Using the Hybrid Kalman Cuckoo Search tracker, we can efficiently explore the search space in order to locate an object?s position from one frame to the next. The Lévy flight model is also modified in order to re-adapt the Lévy step size as the algorithm approaches the desired solution. The Hybrid Kalman Cuckoo Search tracker is tested on a variety of datasets including one where we incorporated different situations, as well as some videos from the CAVIAR, SPEVI, and other datasets. The comparative study results show that the proposed algorithm outperforms the Particle Swarm Optimization based tracker, especially in terms of computation time.     

Modelling of a Cd<Subscript>1−x</Subscript>Zn<Subscript>x</Subscript>Te/ZnTe Single Quantum Well for Laser Diodes

In this paper, the carrier density, temperature and quantum well width effect have been investigated for the optical gain for a Cd_1?xZn_xTe/ZnTe Zinc-blend strained quantum well structure. The device emits laser radiations in green–yellow–orange. Our results showed that the optical gain significantly increases with the increasing of the carrier density. It also increases with the decreasing of the Zn concentration, the well width and the temperature. In addition, the optimal threshold current density values were determined for three alloy compositions as 0.7, 0.8 and 0.9.     

Synthesis and antioxidant activities of some novel fluorinated spiro[oxindole-thiazolidine] fused with sulfur and phosphorus heterocycles

Facile routes were achieved for the synthesis of novel fluorinated spiro[oxindole-thiazolidinone] fused with some sulfur and phosphorus heterocycles starting from 5-fluoro-3^′-(4-fluorophenyl)-4^′H-spiro[indole-3,2^′-thiazolidine]-2,4^′(1H)-dione (1) via its reaction with trifluoroacetamide, 2-chloro-6-fluorobenzaldehyde and hydrazine hydrate followed by treatment with some suitable sulfur and phosphorus reagents. The antioxidant activities of the synthesized compounds were also evaluated.     

The effects of nephrectomy on the developing fetus

Bilateral renal pathologies such as renal agenesis and renal dysplasia and lower urinary tract obstruction have been reported to result in pulmonary hypoplasia. Although oligohydramnios and resultant thoracic compression was suggested to be the cause of pulmonary hypoplasia, the exact mechanism is still unknown. Additionally the effect of absence of renal tissue on the development of the fetus has not been previously studied in detail. Therefore an experimental study was planned to investigate the effects of fetal nephrectomy on development. The fetuses from 27 New Zealand white rabbits were studied on the 23rd day of gestation. Right ovarian-end fetuses underwent bilateral nephrectomy or sham operations. Rabbits underwent hysterectomy on gestational day 30, and live fetuses were studied. Fetal body, lung, heart and liver weights, and lung, heart and thorax volumes were determined, organ weight/body weight ratios were calculated. Additionally, lungs were evaluated by histological examination. Although fetal nephrectomy resulted in decreased body weight (BW), lung, heart, liver weights and heart weight/BW ratio (p < 0.05), lung weight/BW and liver weight/BW ratios did not differ. Additionally, heart and thorax volumes were significantly decreased in the nephrectomy group (p < 0.05). However lung volume and thorax volume/BW ratio did not differ between groups. The histological evaluation of lungs revealed exfoliated cells but normal lung development. Bilateral fetal nephrectomy results in small-for-gestational age (SGA) status during birth without affecting the development of organic systems. Since SGA status may be associated with decreased placentofetal blood flow, bilateral nephrectomy may act through decreasing placentofetal blood flow and/or through the lack of kidney-related growth factors.     

Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction

The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 s and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.