Temperature/pH/magnetic triple‐sensitive nanogel–hydrogel nanocomposite for release of anticancer drug

Hydrogels, nanogels and nanocomposites show increasing potential for application in drug delivery systems due to their good chemical and physical properties. Therefore, we were encouraged to combine them to produce a new compound with unique properties for a long‐term drug release system. In this regard, the design and application of a nanocomposite hydrogel containing entrapped nanogel for drug delivery are demonstrated. To this aim, we first prepared an iron oxide nanocomposite nanogel based on poly(N‐isopropylacrylamide)‐co‐((2‐dimethylaminoethyl) methacrylate) (PNIPAM‐co‐PDMA) grafted onto sodium alginate (NaAlg) as a biocompatible polymer and iron oxide nanoparticles (ION) as nanometric base (PND/ION‐NG). This was then added into a solution of PDMA grafted onto NaAlg. Through dropwise addition of mixed aqueous solution of iron salts into the prepared polymeric solution, a novel hydrogel nanocomposite with excellent pH, thermal and magnetic responsivity was fabricated. The synthesized samples were fully characterized using Fourier transform infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy with energy‐dispersive X‐ray analysis, vibrating sample magnetometry and atomic force microscopy. A mechanism for the formation of PNIPAM‐co‐PDMA/NaAlg‐ION nanogel–PDMA/NaAlg‐ION hydrogel and PND/ION nanogel is suggested. Swelling capacity was measured at various temperatures (25 to 45 °C), pH values (from 2 to 11) and magnetic field and under load (0.3 psi) and the dependence of swelling properties of the nanogel–hydrogel nanocomposite on these factors was well demonstrated. The release rate of doxorubicin hydrochloride (DOX) as an anticancer drug was studied at different pH values and temperatures in the presence and absence of a magnetic field. The results showed that these factors have a high impact on drug release from this nanocomposite. The result showed that DOX release could be sustained for up to 12.5 days from these nanocomposite hydrogels, significantly longer than that achievable using the constituent hydrogel or nanogel alone (<1 day). The results indicated that the nanogel–hydrogel nanocomposite can serve as a novel nanocarrier for anticancer drug delivery. © 2019 Society of Chemical Industry     

A four-bias three-layer graphene-based THz absorber

Wave absorbers are considered to be fundamental building blocks for the manipulation of light. Almost all optical systems exploit absorbers to realize some functions. A highly tunable wide-band THz absorber is presented herein. Utilizing a dual-bias scheme with a single graphene layer leads to greater freedom to control the absorption response, while a conventional periodic array of graphene ribbons and a layer of graphene sheet are also exploited. Also, a circuit model representation for all the constituent parts of the proposed absorber is developed with an evolved design methodology. According to the simulation results, wide-band absorption from 3.5 to 6 THz is achieved.

     

Privacy-preserving biometric verification with outsourced correlation filter computation

Multimedia Tools and Applications - In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage,...     

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.     

Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non-uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region-based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U-Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state-of-the-art methods in brain tumor segmentation.     

A novel method for voice conversion based on non-parallel corpus

This article puts forward a new algorithm for voice conversion which not only removes the necessity of parallel corpus in the training phase but also resolves the issue of insufficiency of the target speaker’s corpus. The proposed approach is based on one of the new voice conversion models utilizing classical LPC analysis-synthesis model combined with GMM. Through this algorithm, the conversion functions among vowels and demi-syllables are derived. We assumed that these functions are rather the same for different speakers if their genders, accents, and languages are alike. Therefore, we will be able to produce the demi-syllables with just having access to few sentences from the target speaker and forming the GMM for one of his/her vowels. The results from the appraisal of the proposed method for voice conversion clarifies that this method has the ability to efficiently realize the speech features of the target speaker. It can also provide results comparable to the ones obtained through the parallel-corpus-based approaches.     

Integrated Fuzzy DEA-ANFIS to Measure the Success Effect of Human Resource Spirituality

Workplace spirituality has gained attention as it is proven to be a contributor to organizational performance improvement. This paper aims to assess the impact of human resource spirituality on the success of organizational strategic change projects. The success of the projects is measured by the well-known criterion of deviation from the planned budget cost. Data collection is based on a questionnaire survey of 252 personnel in 36 large and medium-scale organizational change projects in power industry. The paper proposes an integrated algorithm of fuzzy data envelopment analysis (FDEA) and adaptive network-based fuzzy inference system (ANFIS) for measuring the pure effect of human resource spirituality on the success of organizational change projects in the power industry. It also achieves a verified tool capable of addressing complexity, nonlinearity, ambiguity, and fuzziness for measuring spirituality of human resources in the projects. Results show that spirituality of the project team has a significant effect on project success.     

Improvement of time alignment of the speech signals to be used in voice conversion

One of the main applications of time alignment is parallel corpus based voice conversion. In the literature, various methods such as dynamic time warping (DTW) and hidden Markov model have been suggested for time alignment of two speech signals. In this paper, we introduce some modifications to DTW in order to decrease the time alignment error. These modifications are refinement, which is done by exerting a threshold, normalization, and comparisons between the preceding and the following frames to make sound correspondence between two different parallel corpus-based speakers’ speeches. Evaluation of this approach which has been done on some corpus sentences indicates a significant improvement of time alignment. At least about 4% and in some cases 15% decrease of error in comparison with DTW has been achieved.