Ultrasound‐Propelled Nanoporous Gold Wire for Efficient Drug Loading and Release

Ultrasound (US)‐powered nanowire motors based on nanoporous gold segment are developed for increasing the drug loading capacity. The new highly porous nanomotors are characterized with a tunable pore size, high surface area, and high capacity for the drug payload. These nanowire motors are prepared by template membrane deposition of a silver‐gold alloy segment followed by dealloying the silver component. The drug doxorubicin (DOX) is loaded within the nanopores via electrostatic interactions with an anionic polymeric coating. The nanoporous gold structure also facilitates the near‐infrared (NIR) light controlled release of the drug through photothermal effects. Ultrasound‐driven transport of the loaded drug toward cancer cells followed by NIR‐light triggered release is illustrated. The incorporation of the nanoporous gold segment leads to a nearly 20‐fold increase in the active surface area compared to common gold nanowire motors. It is envisioned that such US‐powered nanomotors could provide a new approach to rapidly and efficiently deliver large therapeutic payloads in a target‐specific manner.     

An integer programming-based search technique for error-prone structures of LDPC codes

In this paper, an efficient, general framework is presented for finding common, devastating error-prone structures (EPS) of any finite-length low-density parity-check (LDPC) code. The smallest stopping set for the binary erasure channel (BEC), the smallest fully absorbing set, the smallest absorbing set, and the smallest elementary trapping set for the binary symmetric channel (BSC) are found and the dominant EPS are enumerated. The method involves integer programming optimization techniques, which guarantees that the results are provably optimal.     

Storage stability of cod liver oil organogels formed with beeswax and carnauba wax

In this study, organogels of cod liver oil (CLO) with beeswax (BW) and carnauba wax (CW) were prepared, and compared with a commercial margarine (CM). Oil binding capacities (OBC) of BW organogels were over 99%, while CW had a maximum OBC value of 91.28%. Crystal formation time of BW was shorter. Although the highest solid fat content (SFC) was in the 10% CW containing sample (8.69%), it was 28.99% in the CM sample at 20 °C. The peak melting temperature of CM was 43.70 °C, and BW organogel at 3% addition had the closest values (45.42 °C). Firmness and stickiness values of the organogels were lower than that of CM sample. No significant change in the texture parameters during storage was detected, indicating good stability. There was no hurdle against oxidation by organogelation during storage. This study has shown that CLO organogels can be suitable spreadable products.     

Fossil fuel sustainability index: An application of resource management

A brief review on use of fossil fuel resources and sustainability is given in this paper. A sustainability index for fossil fuels is developed, which aims to determine the most efficient management of fossil fuel resources for the energy system. The study is conducted for 62 countries, in the presence of independence, lifetime and environmental constraints. The effect of these indicators are then integrated into a single index for oil, natural gas, and coal. Two approaches have been taken. The first one employs equally weighing of each index, where the second one weighs the indices by using principle component analysis. It is concluded that Fossil Fuel Sustainability Index (FFSI) values indicate that countries supporting oil as the one and only major player are condemned to suffer due to incompetent energy policies.     

Sulfite determination using sulfite oxidase biosensor based glassy carbon electrode coated with thin mercury film

The preparation of sulfite biosensor by immobilization of sulfite oxidase on a glassy carbon electrode coated with mercury thin film is described. Dissolved oxygen can be readily reduced at the glassy carbon electrode coated with mercury thin film. The working principle of the biosensor was based on monitoring decrease in the peak current due to the depletion in dissolved oxygen concentration according to the reaction of sulfite oxidase.     

Infrared and Structural Properties of Y1−xNdxBa2Cu3O7−δ (0 ≤ x ≤ 1)

Infrared and structural properties of Y_1?x Nd _x Ba₂Cu₃O_7?δ (0 ≤ x ≤ 1) were investigated using infrared absorption spectroscopy and X-ray powder diffraction. The unit cell parameters of the samples were defined using X-ray diffraction data. The resistance measurements showed that the samples revealed superconductivity in the temperature range of 80–100 K. It was observed that by the substitution of Nd to Y in YBa₂Cu₃O_{7 ? δ} IR band at 573 cm^?1 that is assigned as Cu–O axial antisymmetric stretching mode shifts to 533 cm^?1 while the band at 620 cm^?1 that is due to Cu–O symmetric stretching mode in YBa₂Cu₃O_7?δ shifts to 588 cm^?1.     

From classic observability to a simple fuzzy observability for fuzzy discrete-event systems

In order to determine uncertainties from restricted available information, fuzzy discrete-event systems (FDESs), or fuzzy discrete-event dynamic systems (FDEDSs), were recently proposed. These frameworks include fuzzy states and events occurring simultaneously with different membership degrees. Fuzzy states and events have been used to describe uncertainties that occur often in practical problems, such as treatment planning for HIV/AIDS patients, sensory information processing for robotic control, and fault diagnosis problems. In order to measure information associated with FDESs or FDEDSs, the classical discrete event system (DES) observability has been turned into fuzzy observability for FDESs or FDEDSs. The newly proposed method allows ease of defining degrees of observability so that uncertainties in FDESs or FDEDSs can be dealt with effectively. This gives an opportunity to design better decision-making systems. To calculate the observability degree, a simple fuzzy observability checking method is introduced, and two examples are elaborated upon to illustrate the presented method. Finally, the newly proposed method is tested on a heating, ventilating, and air-conditioning (HVAC) system.     

Super-resolution reconstruction of faces by enhanced global models of shape and texture

We present a computationally efficient method for the super-resolution reconstruction of face images from their low-resolution versions. It is based on generative models and utilizes both the shape and texture components together. The main idea is that the image details can be synthesized by global modeling of accurately aligned local image regions. In order to achieve sufficient accuracy in alignment, shape reconstruction is considered as a separate problem and solved together with texture reconstruction in a coordinated manner. Meanwhile, the statistical dependency between the shape and texture components is also considered. Moreover, different from traditional model-based super-resolution methods, we use a corrected form of the degradation operator with the aligned images. We show that when the degradation is used with the aligned texture components as is, it causes bias in the reconstructions. To overcome this problem, we reflect the same processing performed in alignment onto the degradation operator and use this corrected version in texture reconstruction. Experimental results show that the proposed solution provides superior image reconstructions (both qualitatively and quantitatively) in a faster way.     

Maximum likelihood estimation of Gaussian mixture models using stochastic search

Gaussian mixture models (GMM), commonly used in pattern recognition and machine learning, provide a flexible probabilistic model for the data. The conventional expectation–maximization (EM) algorithm for the maximum likelihood estimation of the parameters of GMMs is very sensitive to initialization and easily gets trapped in local maxima. Stochastic search algorithms have been popular alternatives for global optimization but their uses for GMM estimation have been limited to constrained models using identity or diagonal covariance matrices. Our major contributions in this paper are twofold. First, we present a novel parametrization for arbitrary covariance matrices that allow independent updating of individual parameters while retaining validity of the resultant matrices. Second, we propose an effective parameter matching technique to mitigate the issues related with the existence of multiple candidate solutions that are equivalent under permutations of the GMM components. Experiments on synthetic and real data sets show that the proposed framework has a robust performance and achieves significantly higher likelihood values than the EM algorithm.