Characterization,in vitro bioactivity and biological studies of sol-gel-derived TiO2 substituted 58S bioactive glass

Bioactive glasses (BGs) have been used for bone formation and bone repair processes in recent years. This study investigated the titanium substitution effect on 58S BGs (Ti-BGs) 60SiO₂-(36 − X)CaO-4P₂O₅-XTiO₂ (X = 0, 3, and 5 mol.%) prepared by the sol-gel technique, and the main goal was to find the optimum amount of titanium in Ti-BGs. Synthesized BGs, which were investigated after immersion in simulated body fluid (SBF), were tested by X-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy. Moreover alkaline phosphate (ALP) activity, 3-(4,5dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and antibacterial studies were employed to investigate the biological properties of Ti-BGs. According to the FTIR and XRD test results, hydroxyapatite (HA) formation on Ti-BGs surfaces was confirmed. Meanwhile, the presence of 5 mol.% compared to 3 mol.% increased the HA grain distribution and their size on the Ti-BGs surface. Additionally, MTT and ALP results confirmed that the optimal amount of titanium substitution in BG was 5 mol.%. Since 5 mol.% Ti incorporated BG (BG-5) had the highest biocompatibility level, antibacterial properties, maximum cell proliferation, and ALP activity among the synthesized Ti-BGs, it is presented as the best candidate for further in vivo investigations.     

Berechnung der Wirbelstromdichten und-kräfte in der Umgebung großer supraleitender Spulen für den magnetischen Einschluß bei der Kernfusion

Übersicht Um die Betriebssicherheit großer supraleitender Magnetsysteme für die Kernfusionsforschung zu gewährleisten, muß die im Magnetsystem gespeicherte Energie schnell entladen werden können. Durch die veränderlichen großvolumigen Magnetfelder werden in benachbarten leitfähigen Strukturen durch Induktion hohe Wirbelströme hervorgerufen. Durch sie treten dort elektromagnetische Kräfte. auf, die einzelne Komponenten mechanisch überlasten können. Zur Berechnung dieser Kräfte wurde eine einfache numerischanalytische Methode entwickelt, die im folgenden vorgestellt und mit Finite-Elemente-Verfahren verglichen wird.

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Calculation of eddy current densities and forces around large superconducting coils for nuclear fusion

Contents The reliable operation of the superconducting magnet systems used for the magnetic confinement in nuclear fusion research requires the ability of a fast discharge of the stored magnetic energy. The corresponding magnetic field variation gives rise to high eddy currents by inductive coupling with surrounding conductive components. The eddy current forces can lead to a mechanical overload of some of these components. A simple numerical-analytical method was developed in order to calculate these forces. This method is presented and compared with the Finite Element Method.

     

Learning science via animated movies: Its effect on students’ thinking and motivation

Some researchers claim that animations may hinder students’ meaningful learning or evoke misunderstandings. In order to examine these assertions, our study investigated the effect of animated movies on students’ learning outcomes and motivation to learn. Applying the quantitative methodology, two pre- and post-questionnaires were administered: Science thinking skills and Motivation to learn science. Students’ overall achievement in science was examined by their report card scores. The research population (N = 1335) was divided into experimental (N = 926) and control (N = 409) groups from 11 elementary schools. Findings indicated that the use of animated movies promoted students’ explanation ability and their understanding of scientific concepts. Findings also indicated that students who studied science with the use of animated movies developed higher motivation to learn science, in terms of: self-efficacy, interest and enjoyment, connection to daily life, and importance to their future, compared to the control students. Following the definition of multimedia, the students who study with the use of animated movies, applied all three learning styles: visual, auditory and kinesthetic. The use of multimedia and the fact that the students were engaged in exploring new concepts, that were relevant to their daily life experiences, can explain the positive results.     

Accelerating Boyer–Moore searches on binary texts

The Boyer and Moore (BM) pattern matching algorithm is considered as one of the best, but its performance is reduced on binary data. Yet, searching in binary texts has important applications, such as compressed matching. The paper shows how, by means of some pre-computed tables, one may implement the BM algorithm also for the binary case without referring to bits, and processing only entire blocks such as bytes or words, thereby significantly reducing the number of comparisons. Empirical comparisons show that the new variant performs better than regular binary BM and even than BDM.     

Adaptive ROC-based ensembles of HMMs applied to anomaly detection

Hidden Markov models (HMMs) have been successfully applied in many intrusion detection applications, including anomaly detection from sequences of operating system calls. In practice, anomaly detection systems (ADSs) based on HMMs typically generate false alarms because they are designed using limited amount of representative training data. Since new data may become available over time, an important feature of an ADS is the ability to accommodate newly acquired data incrementally, after it has originally been trained and deployed for operations. In this paper, a system based on the receiver operating characteristic (ROC) is proposed to efficiently adapt ensembles of HMMs (EoHMMs) in response to new data, according to a learn-and-combine approach. When a new block of training data becomes available, a pool of base HMMs is generated from the data using a different number of HMM states and random initializations. The responses from the newly trained HMMs are then combined to those of the previously trained HMMs in ROC space using a novel incremental Boolean combination (incrBC) technique. Finally, specialized algorithms for model management allow to select a diversified EoHMM from the pool, and adapt Boolean fusion functions and thresholds for improved performance, while it prunes redundant base HMMs. The proposed system is capable of changing the desired operating point during operations, and this point can be adjusted to changes in prior probabilities and costs of errors. Computer simulations conducted on synthetic and real-world host-based intrusion detection data indicate that the proposed system can achieve a significantly higher level of performance than when parameters of a single best HMM are estimated, at each learning stage, using reference batch and incremental learning techniques. It also outperforms the learn-and-combine approaches using static fusion functions (e.g., majority voting). Over time, the proposed ensemble selection algorithms form compact EoHMMs, while maintaining or improving system accuracy. Pruning allows to limit the pool size from increasing indefinitely, thereby reducing the storage space for accommodating HMMs parameters without negatively affecting the overall EoHMM performance. Although applied for HMM-based ADSs, the proposed approach is general and can be employed for a wide range of classifiers and detection applications.     

Electric vehicle energy consumption modelling and estimation—A case study

Electric vehicles (EVs) have a limited driving range compared to conventional vehicles. Accurate estimation of EV's range is therefore a significant need to eliminate “range anxiety” that refers to drivers' fear of running out of energy while driving. However, the range estimators used in the currently available EVs are not sufficiently accurate. To overcome this issue, more accurate range estimation techniques are investigated. Nonetheless, an accurate power‐based EV energy consumption model is crucial to obtain a precise range estimation. This paper describes a study on EV energy consumption modelling. For this purpose, EV modelling is carried out using MATLAB/Simulink software based on a real EV in the market, the BMW i3. The EV model includes vehicle powertrain system and longitudinal vehicle dynamics. The powertrain is modelled using efficiency maps of the electric motor and the power electronics' data available for BMW i3. It also includes a transmission and a battery model (ie, Thevenin equivalent circuit model). A driver model is developed as well to control the vehicle's speed and to represent human driver's behaviour. In addition, a regenerative braking strategy, based on a series brake system, is developed to model the behaviour of a real braking controller. Auxiliary devices are also included in the EV model to improve energy consumption estimation accuracy as they can have a significant impact on that. The vehicle model is validated against published energy consumption values that demonstrates a satisfactory level of accuracy with 2% to 6% error between simulation and experimental results for Environmental Protection Agency and NEDC tests.     

Apoferritin Modified Magnetic Particles as Doxorubicin Carriers for Anticancer Drug Delivery

Magnetic particle mediated transport in combination with nanomaterial based drug carrier has a great potential for targeted cancer therapy. In this study, doxorubicin encapsulation into the apoferritin and its conjugation with magnetic particles was investigated by capillary electrophoresis with laser-induced fluorescence detection (CE-LIF). The quantification of encapsulated doxorubicin was performed by fluorescence spectroscopy and compared to CE-LIF. Moreover, the significant enhancement of the doxorubicin signal was observed by addition of methanol into the sample solution.