Shape Transformable Strategies for Drug Delivery

In tumor therapy, nanodrug delivery systems have gained momentum in the last decade. However, its efficacy remains insufficient for clinical applications. The physical properties of nanoparticles, including size, shape, and surface characteristics, can strongly affect the delivery efficacy. Ironically, research on shape function is relatively scarce, although the nanoparticle shape greatly impacts their performance; for example, nanorods with a high aspect ratio (AR) achieve greater accumulation, but their penetration is relatively weak. Hence, rather than selecting a suitable AR to balance them, the strategy of a transformable AR (i.e., transformation) is ideal in this case. Nanoparticle transformation can be achieved by either internal stimuli (such as pH and enzymes) or external stimuli (such as light) spatially and temporally with precision, thereby dramatically enhancing the efficiency of drug delivery. Thus, nanoparticle transformation is becoming a promising prospect for improving cancer treatment. In this review, first, the effect of shape on drug delivery is summarized, then, the recently transformable drug delivery systems are reviewed, and finally, the future direction of shape-transformable nanoparticles in tumor therapy is discussed.     

Multi-focus images fusion via residual generative adversarial network

Multimedia Tools and Applications - Recently, most existing learning-based fusion methods are not fully end-to-end, which still predict the decision map and recover the fused image by the refined...     

Global existence of three-dimensional incompressible magneto-micropolar system with mixed partial dissipation,magnetic diffusion and angular viscosity

The magneto-micropolar fluid flows describe the motion of electrically conducting micropolar fluids in the presence of a magnetic field. The issue of whether the strong solution of magneto-micropolar equations in three-dimensional can exist globally in time with large initial data is still unknown. In this paper, we deal with the Cauchy problem of the three-dimensional magneto-micropolar system with mixed partial dissipation, magnetic diffusion and angular viscosity. More precisely, the global existence of smooth solutions to the three-dimensional incompressible magneto-micropolar fluid equations with mixed partial dissipation, magnetic diffusion and angular viscosity are obtained by energy method under the assumption that $H^1$-norm of the initial data $(u_0,b_0,w_0)$ sufficiently small, namely $6u_0,b_0,{\omega }_0{6}_{H^1\left({\mathbb{R}}^3\right)}^2\le \frac{\epsilon }{2}$, where $\epsilon$ is a sufficiently small positive number. This work follows the techniques in the paper of Cao and Wu (2011).     

Fractional Wishart processes and ε-fractional Wishart processes with applications

In this paper, we introduce two new matrix stochastic processes: fractional Wishart processes and $\epsilon$-fractional Wishart processes with integer indices which are based on the fractional Brownian motions and then extend $\epsilon$-fractional Wishart processes to the case with non-integer indices. Both processes include classic Wishart processes (if the Hurst index $H$ equals $\frac{1}{2}$) and present serial correlation of stochastic processes. Applying $\epsilon$-fractional Wishart processes to financial volatility theory, the financial models account for the stochastic volatilities of the assets and for the stochastic correlations not only between the underlying assets’ returns but also between their volatilities and for stochastic serial correlation of the relevant assets.     

Clustering based multiple branches deep networks for single image super-resolution

Since the limitation of optical sensors, it’s often hard to obtain an image with the ideal resolution. Image super-resolution (SR) technology can generate a high-resolution image from the corresponding low-resolution image. Recently, deep learning (DL) based SR methods draw much attention due to their satisfying reconstruction results. However, these methods often neglect the diversity of image patches. Therefore, the reconstruction effect is limited. To fully exploit the texture variability across different image patches, we propose a universal, flexible, and effective framework. The proposed framework can be adopted to any DL based methods. It can significantly improve the SR accuracy while maintaining the running time. In the proposed framework, K-means is employed to cluster image patches into different categories. Multiple CNN branches are designed for these different categories to reconstruct the SR image. Each branch is weighted in accordance with the Euclidean distance to the cluster centers. Experimental results demonstrate that by applying the proposed framework, performance of the DL based SR method can be significantly improved.

     

Multi-objective colonial competitive algorithm for hybrid flowshop problem

This study analyses the multi-objective optimization in hybrid flowshop problem, in which two conflicting objectives, makespan and total weighted tardiness, are considered to be minimized simultaneously. The multi-objective version of Colonial Competitive Algorithm (CCA) for real world optimization problem is introduced and investigated. In contrast to multi-objective problems solved by CCA, presented in the literature, which used the combination of the objectives as single objective, the proposed algorithm is established on Pareto solutions concepts. Another novelty of this paper is estimating the power of each imperialist by a probabilistic criterion for this multi objective algorithm. Besides that, the variable neighborhood search is implemented as an assimilation strategy. Performance of the algorithm is finally compared with a famous algorithm for scheduling problem, NSGA-II, and the multi-objective form of CCA [28].     

Enabling real-time information service on telehealth system over cloud-based big data platform

A telehealth system covers both clinical and nonclinical uses, which not only provides store-and-forward data services to be offline studied by relevant specialists, but also monitors the real-time physiological data through ubiquitous sensors to support remote telemedicine. However, the current telehealth systems do not consider the velocity and veracity of the big-data system in the medical context. Emergency events generate a large amount of the real-time data, which should be stored in the data center, and forwarded to remote hospitals. Furthermore, patients’ information is scattered on the distributed data center, which cannot provide a high-efficient remote real-time service. In this paper, we proposes a probability-based bandwidth model in a telehealth cloud system, which helps cloud broker to provide a high performance allocation of computing nodes and links. This brokering mechanism considers the location protocol of Personal Health Record (PHR) in cloud and schedules the real-time signals with a low information transfer between different hosts. The broker uses several bandwidth evaluating methods to predict the near future usage of bandwidth in a telehealth context. The simulation results show that our model is effective at determining the best performing service, and the inserted service validates the utility of our approach.     

An improved vector quantization method using deep neural network

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR).     

A novel anti-intercepting wireless communication scheme

In this paper, a novel wireless communication scheme is proposed, which has high anti-intercepting ability without the frequency efficiency loss. In this scheme, we use the baseband mixed signal to construct the transmission signal, which is consisted of two independent and useful baseband signals. According to our analysis, we find that the baseband mixed signal transmission scheme can achieve higher anti-intercepting ability than the existing mixed signal schemes, and it can achieve high frequency efficiency since the mixed signals carry useful information simultaneously. We also provide a low-complexity reception algorithm based Gibbs sampling for the proposed scheme. According to simulation results, we demonstrate that the proposed scheme is effective, and validate its anti-intercepting ability.