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.     

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.

     

Polymer/bioactive glass nanocomposites for biomedical applications: A review

Nanoscale bioactive glasses have been gaining attention due to their reported superior osteoconductivity when compared to conventional (micron-sized) bioactive glass materials. The combination of bioactive glass nanoparticles or nanofibers with polymeric systems enables the production of nanocomposites with potential to be used in a series of orthopedic applications, including scaffolds for tissue engineering and regenerative medicine. This review presents the state of art of the preparation of nanoscale bioactive glasses and corresponding composites with biocompatible polymers. The recent developments in the preparation methods of nano-sized bioactive glasses are reviewed, covering sol–gel routes, microemulsion techniques, gas phase synthesis method (flame spray synthesis), laser spinning, and electro-spinning. Then, examples of the preparation and properties of nanocomposites based on such inorganic bionanomaterials are presented, obtained using various polymer matrices, including polyesters such as poly(hydroxybutyrate), poly(lactic acid) and poly(caprolactone), and natural-based polymers such as polysaccharides (starch, chitin, chitosan) or proteins (silk fibroin, collagen). The physico-chemical, mechanical, and biological advantages of incorporating nanoscale bioactive glasses in such biodegradable nanocomposites are discussed and the possibilities to expand the use of these materials in other nanotechnology concepts aimed to be used in different biomedical applications are also highlighted.     

Partial purification of <Emphasis Type="Italic">nigella sativa</Emphasis> L. Seed lipase and its application in hydrolytic reactions. Enrichment of γ-linolenic acid from borage oil

Selective hydrolysis of borage (Borago officinalis L.) oil was catalyzed by two lipase preparations of Nigella sativa L. seeds at 40°C in a mixture of borage oil, water, and hexane. Ammonium sulfate-precipitated lipase (Nigella PL) and lipase partially purified by DEAE-ion exchange chromatography (Nigella CPL) exhibited a negative specificity toward γ-linolenic acid (GLA). Best results were obtained in the experiments conducted with 330 U/g oil of Nigella PL and 200 U/g oil of nigella CPL. When 330 U/g oil of Nigella PL was used, after 8 h the GLA level rose from 21.9% in the starting oil to 29.6 and 41.8% in TAG and DAG fractions of the product mixtures, respectively (1.5-fold enrichment of GLA in the total unhydrolyzed acylglycerol fraction). At 200 U/g oil enzyme concentration of Nigella CPL, after 77 h maximum GLA enrichment was observed in the DAG fraction. The GLA content of the DAG increased to 34.6%, corresponding to almost 1.6-fold enrichment. The relative inability of Nigella sativa lipase(s) to hydrolyze γ-linolenoyl moieties of TAG can be used for the enrichment of this acid in the unhydrolyzed acylglycerol fractions of GLA-containing oils.     

The effects of instructional support and learner interests when learning using computer simulations

Within the scope of this study, the effectiveness of two kinds of instructional support was evaluated with regard to the learner’s interests. Two versions of a simulation program about the respiratory chain were developed, differing only in the kind of tasks provided for instructional support: One version contained problem-solving tasks, the other one contained worked-out examples. The focus was on the learner’s interest in the subject and in computers. The first goal of the study was to find to what extent computer simulations incorporating the different kinds of instructional support have positive effects on situational subject-interest. The second goal was to evaluate the interactions between the learner’s interests and the instructional support with regard to the learning results (subdivided into factual knowledge and understanding). Simulations with worked-out examples were shown to have positive effects on the learner’s situational interest in the subject. This was not found to be the case in simulations with problem-solving tasks. Regardless of the kind of instructional support, learners with little interest in the subject were able to achieve significant gains in factual knowledge. However, improvement in understanding was dependent on the kind of instructional support.