Trends in overweight among US adults from 1987 to 1993: a multistate telephone survey

OBJECTIVES: Using data from the Behavioral Risk Factor Surveillance System, this study describes trends in the prevalence of overweight between 1987 and 1993. METHODS: Data were examined from 33 states participating in an ongoing telephone survey of health behaviors of adults (n = 387,704). Self-reported weights and heights were used to calculate sex-specific prevalence estimates of overweight for each year from 1987 to 1993. Time trends were evaluated with the use of linear regression. RESULTS: Between 1987 and 1993, the age-adjusted prevalence of overweight increased by 0.9% per year for both sexes (from 21.9% to 26.7% among men and from 20.6% to 25.4% among women). The increasing linear trend was observed in all subgroups of the population but was most notable for Black men (1.5% per year) and men living in the Northeast (1.4% per year). Secular changes in smoking and leisure-time physical activity did not entirely account for the increase in overweight. CONCLUSIONS: The prevalence of overweight among American adults increased by 5% between 1987 and 1993. Efforts are needed to explore the causes of this adverse trend and to find effective strategies to prevent obesity.     

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

     

Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).     

Visualization of fibrous and thread-like data

Thread-like structures are becoming more common in modern volumetric data sets as our ability to image vascular and neural tissue at higher resolutions improves. The thread-like structures of neurons and micro-vessels pose a unique problem in visualization since they tend to be densely packed in small volumes of tissue. This makes it difficult for an observer to interpret useful patterns from the data or trace individual fibers. In this paper we describe several methods for dealing with large amounts of thread-like data, such as data sets collected using Knife-Edge Scanning Microscopy (KESM) and Serial Block-Face Scanning Electron Microscopy (SBF-SEM). These methods allow us to collect volumetric data from embedded samples of whole-brain tissue. The neuronal and microvascular data that we acquire consists of thin, branching structures extending over very large regions. Traditional visualization schemes are not sufficient to make sense of the large, dense, complex structures encountered. In this paper, we address three methods to allow a user to explore a fiber network effectively. We describe interactive techniques for rendering large sets of neurons using self-orienting surfaces implemented on the GPU. We also present techniques for rendering fiber networks in a way that provides useful information about flow and orientation. Third, a global illumination framework is used to create high-quality visualizations that emphasize the underlying fiber structure. Implementation details, performance, and advantages and disadvantages of each approach are discussed.     

THE EFFECT ON THE THERMAL EFFICIENCY OF LIME ADDED TO THE COMBUSTION CHAMBER FOR RETENTION ON SULPHUR

ABSTRACT

In order to prevent the emission of SO_x, with the flue gas, certain amount of the sulphur of the coal can be retained by adding an adequate amount of lime in the combustion chamber. In this study, two series of experiments were conducted by adding lime at the molar ratios of CaO/S = 0·5 to 2·0 using Ankara-Beypazan lignites, which have around 5 % sulphur content as received form. In one of the series lime was added directly in to the lump coal, and in the other, coal was briquetted together with lime using molasses as binder. It was observed from the experimental results that the increase in the lime ratio leads to an increase in the retained sulphur percentage while it leads to a considerable decrease in thermal efficiency. In order to retain 50 % of sulphur, it was necessary to add lime into lump coal at a CaO/S molar ratio of at least 1,1. This minimum ratio was 0·70 for briquetted coal. When lime was added at these ratios, the thermal efficiencies in the first case dropped to as low as 82 % and to 92 % in the second.     

Effect of intracanal medicaments on the push-out bond strength of Biodentine in comparison with Bioaggregate apical plugs

To evaluate the effect of intracanal medicaments on the push-out bond strength of Biodentine in comparison with DiaRoot BioAggregate (BA) when used as apical plugs. Forty single-rooted teeth were prepared using Peeso reamers. The samples were divided into four groups. The intracanal medicaments were applied to the root canals as follows: Group1: a combination of metronidazole–ciprofloxacin–cefaclor, Group2: a combination of metronidazole–ciprofloxacin, Group3: calcium hydroxide, and Group4: no medication. After 21 days, the medicaments were removed. The apical part of each root was horizontally sectioned into 1-mm thick slices. The samples were divided into two subgroups, and the following materials were placed: Biodentine, DiaRoot-BioAggregate. After 48-h incubation, the push-out bond strength was measured. The data were analyzed by a two-way ANOVA. Biodentine showed a significantly higher mean push-out bond strength value than DiaRoot-BioAggregate (P = 0.00). The medications have an effect on the push-out bond strength of both materials (P = 0.002). Biodentine showed better adhesive performance as an apical plug than DiaRoot-BioAggregate.     

Synthesis,Thermal Properties and Electrical Conductivity of Na-Sialate Geopolymer

This work aims to study the thermal behavior of basic-geopolymers derived from metakaolin (clay). The geopolymers were characterized by different techniques: thermal analysis (DTA, TGA), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and impedance spectroscopy. Some physicochemical properties of the products were also determined: the phases obtained after geopolymer heat treatment and their electrical properties. The results obtained after drying and heat treatment showed that the products kept their initial shapes, but revealed variable colors depending on the temperatures at which they were treated. The products obtained are amorphous between 300 up to 600 °C with peaks relating to the presence of nanocrystallites of muscovites and zeolite, thus at 900 °C it is quite amorphous but only contains nanocrystallites of muscovites. From the temperature of 950 °C, we notice that the geopolymer has been transformed into a crystalline compound predominated by the Nepheline (NaAlSiO₄) with the presence of a crystalline phase by minor peaks of Muscovite, this crystalline character has been increased at 1100 °C to obtain a whole phase crystalline of a Nepheline. The treatment of this geopolymer for one hour at 1200 °C shows an amorphous phase again corresponding to corundum (α-Al₂O₃). This indicates that the dissolution of the grains by the liquid phase induces the conversion of the material structure from sialate [–Si–O–Al–O] to sialate siloxo [–Si–O–Al–O–Si–O–] and the formation of a new crystalline phase (α-Al₂O₃). This development of sialate to sialate-siloxo was confirmed by IR spectroscopy. As mentioned above, from 300 to 900 °C, Na-sialate geopolymer exhibits the same disorder structure of nepheline. The crystal structure of nepheline is characterized by layers of six-membered tetrahedral rings of exclusively oval conformation. The rings are built by Regularly alternating tetrahedral AlO₄ and SiO₄. Stacking the layer’s parallel to the c axis gives a three-dimensional network containing channels occupied by Na cations. This topology favors easy movement of Na⁺ ions throughout the structure. For this reason, ionic migration in nepheline is widely reported. The refinement of Na-Sialate geopolymer at room temperature gives bulk high ionic conductivity of about 5 × 10^?5 S cm^?1 and this is due to the probable joint contribution of H⁺ and Na⁺ ions. Above 200 °C, Na⁺ seems to remain the only charge carrier with a low activation energy of about Ea?=?0.26 eV. At higher temperatures, the characteristic frequencies become so close that it is impossible to distinguish the contributions. A total resistance comprising both grain and grain boundaries contribution is then determined.