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To imitate the composition of natural bone and further improve the biological property of the materials, ZnO/hydroxyapatite/chitosan-polyethylene oxide@gelatin (ZnO/HAP/CS-PEO@GEL) composite scaffolds were developed. The core-shell structured chitosan-polyethylene oxide@gelatin (CS-PEO@GEL) nanofibers which could form the intramolecular hydrogen bond and achieve an Arg-Gly-Asp (RGD) polymer were first prepared by coaxial electrospinning to mimic the extracellular matrix. To further enhance biological activity, hydroxyapatite (HAP) was grown on the surface of the CS-PEO@GEL nanofibers using chemical deposition and ZnO particles were then evenly distributed on the surface of the above composite materials using RF magnetron sputtering. The SEM results showed that chemical deposition and magnetron sputtering did not destroy the three-dimensional architecture of materials, which was beneficial to cell growth. The cell compatibility and proliferation of MG-63 cells on ZnO/HAP/CS-PEO@GEL composite scaffolds were superior to those on CS-PEO@GEL and HAP/CS-PEO@GEL composite scaffolds. An appropriate amount of ZnO sputtering could promote the adhesion of cells on the composite nanofibers. The structure of bone tissue could be better simulated both in composition and in the microenvironment, which provided a suitable environment for cell growth and promoted the proliferation of MG-63 cells. The biomimetic ZnO/HAP/CS-PEO@GEL composite scaffolds were promising materials for bone tissue engineering. [ABSTRACT FROM AUTHOR]

Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection. In this paper, we study a new branch of barrier coverage, namely warning barrier coverage (WBC). Different from the classic barrier coverage, WBC has the inverse protect direction, which moves the sensors surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers. WBC holds a promising prospect in many danger keep out applications for smart cities. For example, a WBC can enclose the debris area in the sea and alarm any approaching ships in order to avoid their damaging propellers. One special feature of WBC is that the target region is usually dangerous and its boundary is previously unknown. Hence, the scattered mobile nodes need to detect the boundary and form the barrier coverage themselves. It is challenging to form these distributed sensor nodes into a barrier because a node can sense only the local information and there is no global information of the unknown region or other nodes. To this end, in response to the newly proposed issue of the formation of barrier cover, we propose a novel solution AutoBar for mobile sensor nodes to automatically form a WBC for smart cities. Notably, this is the first work to trigger the coverage problem of the alarm barrier, wherein the regional information is not pre-known. To pursue the high coverage quality, we theoretically derive the optimal distribution pattern of sensor nodes using convex theory. Based on the analysis, we design a fully distributed algorithm that enables nodes to collaboratively move toward the optimal distribution pattern. In addition, AutoBar is able to reorganize the barrier even if any node is broken. To validate the feasibility of AutoBar, we develop the prototype of the specialized mobile node, which consists of two kinds of sensors: one for boundary detection and another for visitor detection. Based on the prototype, we conduct extensive real trace-driven simulations in various smart city scenarios. Performance results demonstrate that AutoBar outperforms the existing barrier coverage strategies in terms of coverage quality, formation duration, and communication overhead. [ABSTRACT FROM AUTHOR]

As two of the most important characteristics of workloads, burstiness and self-similarity are gaining more and more attention. Workload generation, which is a key technique for performance analysis and simulations, has also attracted an increasing interest in cloud community in recent years. Though a large number of methods for synthetically generating bursty or self-similar workloads have been proposed in the literature, none of them can deal with workload generation with both of the two characteristics. In this paper, a configurable and intelligible synthetic generator (BURSE) is proposed for bursty and self-similar workloads in cloud computing based on a superposition of two-state Markov Modulated Poisson Processes (MMPP2s). The proposed generator can produce workloads with both specified intension of burstiness and self-similarity. Detailed experimental evaluation demonstrates the accuracy, robustness and good applicability of BURSE. [ABSTRACT FROM AUTHOR]

Schematic illustration of the synthesis and application of A-GQDs and WP5/A-GQDs. [Display omitted] • Green fluorescence A-GQDs were prepared using one-step hydrothermal process. • A-GQDs and WP5/A-GQDs composites all exhibit low cytotoxicity and good biocompatibility. • A-GQDs have a bright fluorescence imaging on the cell membranes of cancer cells and normal cells. • A-GQDs modified by WP5 have good penetration into cell membranes and excellent fluorescence performance. Amphiphilic graphene quantum dots (A-GQDs) reveal prodigious potential application in bioimaging, resulting from their hydrophilic and lipophilic properties, as well as their good biocompatibility and negligible cytotoxicity. Herein, green fluorescent A-GQDs with single-layer graphene structure were prepared by one step of hydrothermal method. The A-GQDs with the average size of 3.5 nm displayed excitation-independent emission due to uniform size. A-GQDs emitted bright fluorescence on the surface of the cell membrane of MCF-7 cells when they were co-cultured with MCF-7 cells, which was similar to that of QSG-7701 cells. The water-soluble pillar[5]arene (WP5) was then incorporated on to the A-GQDs via π-π interaction and electrostatic interaction. There was different labeling imaging effect of MCF-7 and QSG-7701 cells achieved by combining the merits of WP5 and A-GQDs compared with individual A-GQDs. The synthesized composites not only had a certain enhancement in fluorescence, but also constructed a penetrating channel through the cell membrane, so that the entire cell had a distinct imaging. Therefore, these composites might be a potential candidate for applications in visualization of cell membrane and bioimaging. [ABSTRACT FROM AUTHOR]

[Display omitted] • The carbon dots were synthesized from the raw materials of citric acid, phenylalanine and anthraquinone. Among them, we selected anthraquinones (1-Aminoanthraquinone or 1, 5-Diaminoanthraquinone) with different numbers of amino groups to explore the effect of the number of amino groups in the raw materials on the carbon dots. The generated CDs had good biocompatibility, and phenylalanine group could bind to the amino acid transporter (LAT1) on the surface of cancer cells which would transport CDs into cancer cells and CDs fluoresced in cancer cell, while it was difficult for CDs to enter normal cells through LAT1. Therefore 1,5-CDs had been used by us to distinguish cancer cells from normal cells. After that, we studied some factors that affected the fluorescence of CDs, and we found that the amino and carboxyl groups on the surface of CDs had certain effects on their fluorescence. When we reduced the amino and carboxyl groups on the surface of CDs, the fluorescence intensity decreased. Moreover, the as-prepared CDs had solvent effects, and the emission peaks of CDs shifted due to the change of solvent polarity. Based on this, we developed a method for the detection of water content in ethanol using CDs as ratiometric probes. The change in the intensity ratio of the two emission peaks corresponded to the change of water content, and the detection linearity was good. • In other studies, carbon dots usually had only one emission peak, and were more susceptible to other environmental factors in practical applications, resulting in large deviations in the results. The phenylalanine-bound CDs prepared in this study had double emission peaks, and we also explored the factors that affected their fluorescence. And phenylalanine-bound CDs could effectively distinguish cancer cells from normal cells and had potential application in detection of H 2 O. Herein, phenylalanine-bound carbon dots (CDs) with dual fluorescence emission peak behavior were reported, one of which was excitation-dependent and the other was not. The amino and carboxyl groups on the surface of CDs had a certain influence on their fluorescence. When the amino and carboxyl groups on the surface of CDs were removed, their fluorescence was significantly weakened. The prepared CDs had solvent effect, and the emission peak of CDs shifted according to the change of the polarity of the solvent. Based on this, a method was developed for detecting water content in ethanol using CDs as a ratio probe. The change in the ratio of the intensity of the two emission peaks corresponded to the change of water content in ethanol. The detection linearity was good. In addition, the resulting CDs had good biocompatibility and could be enormously transported into cancer cells by the amino acid transporter on the surface of cancer cells, while normal cells only had a small amount of CDs through endocytosis, which laid the base for the distinguishing cancer cells from normal cells. The unique fluorescence properties of the prepared CDs achieved their applications in both water detection and cell differentiation. [ABSTRACT FROM AUTHOR]

Air quality prediction is an important research issue due to the increasing impact of air pollution on the urban environment. However, existing methods often fail to forecast high-polluting air conditions, which is precisely what should be highlighted. In this paper, a novel multiple kernel learning (MKL) model that embodies the characteristics of ensemble learning, kernel learning, and representative learning is proposed to forecast the near future air quality (AQ). The centered alignment approach is used for learning kernels, and a boosting approach is used to determine the proper number of kernels. To demonstrate the performance of the proposed MKL model, its performance is compared to that of classical autoregressive integrated moving average (ARIMA) model; widely used parametric models like random forest (RF) and support vector machine (SVM); popular neural network models like multiple layer perceptron (MLP); and long short-term memory neural network. Datasets acquired from a coastal city Hong Kong and an inland city Beijing are used to train and validate all the models. Experiments show that the MKL model outperforms the other models. Moreover, the MKL model has better forecast ability for high health risk category AQ. [ABSTRACT FROM AUTHOR]

With the development of computer technology, computer‐aided drug design (CADD) has become an important means for drug research and development, and its representative method is virtual screening. Various virtual screening platforms have emerged in an endless stream and play great roles in the field of drug discovery. With the increasing number of compound molecules, virtual screening platforms face two challenges: low fluency and low visibility of software operations. In this article, we present an integrated and graphical drug design software eSHAFTS based on three‐dimensional (3D) molecular similarity to overcome these shortcomings. Compared with other software, eSHAFTS has four main advantages, which are integrated molecular editing and drawing, interactive loading and analysis of proteins, multithread and multimode 3D molecular similarity calculation, and multidimensional information visualization. Experiments have verified its convenience, usability, and reliability. By using eSHAFTS, various tasks can be submitted and visualized in one desktop software without locally installing any dependent plug‐ins or software. The software installation package can be downloaded for free at http://lilab.ecust.edu.cn/home/resource.html. © 2019 Wiley Periodicals, Inc. eSHAFTS is an integrated and graphical drug design software based on 3D molecular similarity, covering the stages of target discovery and identification, lead compound discovery, and optimization of drug research. eSHAFTS is expected to help both experts and non‐specialists design molecules for daily research demands, perform virtual screening, target recognition calculations in short periods of time, or analyze molecules using multidimensional information. [ABSTRACT FROM AUTHOR]

Counting pedestrians in surveillance videos has become an urgent safety concern in critical areas. However, surveillance videos of subway spaces suffer from severe crowd occlusion and perspective distortion. In this paper, a novel double-region learning algorithm is presented to overcome these challenges. The main idea of this algorithm is to identify the best two-region boundary and then design a reasonable pedestrian-counting method in each separated region. First, a separate line is obtained via possibility learning, and each frame is divided into a nearby region and a distant region to eliminate the influence of perspective distortion. Second, in the nearby region, we apply the improved aggregate channel feature detection to count the number of pedestrians N 1 . In the distant region, we employ the Extreme Learning Machine and Gaussian Process regression methods to estimate the number of pedestrians N 2 . Finally, the total number of pedestrians in each frame can be obtained with high accuracy according to N 1 and N 2 . We establish a subway pedestrian video dataset about several typical subway stations in Shanghai to validate the algorithm performance. Various experimental results demonstrate that the accuracy of the proposed approach surpasses that of compared methods, which means that our algorithm can meet the management requirements of subway stations. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have been widely used to achieve fine-grained information collection. However, numerous data acquisition and processing of sensors bring some privacy issues. Federated learning is a promising and privacy-friendly framework that trains a model across multiple devices or edge nodes holding local data samples without transferring their data to the server. It is not enough to protect privacy only by maintaining data locality, so differential privacy technology is often used to protect privacy in federated learning. However, different users have different privacy requirements, so it is inappropriate to use the same privacy protection scheme, assuming that all users trust or distrust the server. The former has poor accuracy, while the latter has poor privacy. This paper proposes a secure and reliable federated learning algorithm by integrating hybrid differential privacy into federated learning. We divide users into two categories according to their different privacy needs. In addition, we analyze the convergence and privacy bounds of the proposed algorithm and propose an adaptive gradient clip scheme and improved composition method to reduce the effects of noise and clip, respectively. The validity of the algorithm is verified by theoretical analysis and experimental evaluation on real-world datasets. [ABSTRACT FROM AUTHOR]