Shenyang Ligong University

The insufficient osteogenesis and osseointegration of porous titanium based scaffold limit its further application. Early angiogenesis is important for scaffold survival. It is necessary to develop a multifunctional surface on titanium scaffold with both osteogenic and angiogenic properties. In this study, a biofunctional magnesium coating is deposited on porous Ti6Al4V scaffold. For osseointegration and osteogenesis analysis, in vitro studies reveal that magnesium-coated Ti6Al4V co-culture with MC3T3-E1 cells can improve cell proliferation, adhesion, extracellular matrix (ECM) mineralization and ALP activity compared with bare Ti6Al4V cocultivation. Additionally, MC3T3-E1 cells cultured with magnesium-coated Ti6Al4V show significantly higher osteogenesis-related genes expression. In vivo studies including fluorochrome labeling, micro-computerized tomography and histological examination of magnesium-coated Ti6Al4V scaffold reveal that new bone regeneration is significantly increased in rabbits after implantation. For angiogenesis studies, magnesium-coated Ti6Al4V improve HUVECs proliferation, adhesion, tube formation, wound-healing and Transwell abilities. HUVECs cultured with magnesium-coated Ti6Al4V display significantly higher angiogenesis-related genes (HIF-1α and VEGF) expression. Microangiography analysis reveal that magnesium-coated Ti6Al4V scaffold can significantly enhance the blood vessel formation. This study enlarges the application scope of magnesium and provides an optional choice to the conventional porous Ti6Al4V scaffold with enhanced osteogenesis and angiogenesis for further orthopedic applications.

Surface-enhanced Raman spectroscopy (SERS) has been shown to be able to detect low-concentration biofluids. Saliva SERS readings of 21 lung cancer patients and 20 normal people were measured and differentiated. Most of the Raman peak intensities decrease for lung cancer patients compared with that of normal people. Those peaks were assigned to proteins and nucleic acids, which indicate a corresponding decrease of those substances in saliva. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce and discriminate between the two groups of data, and the study resulted in accuracy, sensitivity, and specificity being 80%, 78%, and 83%, respectively. In conclusion, SERS of saliva showed the ability to predict lung cancer in our experiment.

The localization of sensor nodes is a significant issue in wireless sensor networks (WSNs) because many applications cannot provide services without geolocation data, especially during disaster management. In recent years, a promising unknown-nodes positioning method has been developed that localizes unknown nodes, employing a GPS-enabled mobile anchor node moving in the network, and broadcasting its location information periodically to assist localization. In contrast to most studies on path planning that assume infinite energy of the mobile anchor node, the anchor node in this study, consumes different amounts of energy during phases of startup, turning, and uniform motion considering the aftermath of disasters. To enable a trade-off between location accuracy and energy consumption, a path-planning algorithm combining a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) and SCAN algorithm (SLMAT) is proposed. SLMAT ensures that each unknown node is covered by a regular triangle formed by beacons. Furthermore, the number of corners along the planned path is reduced to save the energy of the mobile anchor node. In addition, a series of experiments have been conducted to evaluate the performance of the SLMAT algorithm. Simulation results indicate that SLMAT outperforms SCAN, LMAT, HILBERT, and Z-curve in terms of localization accuracy and energy consumption.

Underwater acoustic sensor networks (UASNs) are widely used in a variety of ocean applications, such as exploring ocean resources or monitoring abnormal ocean environments. However, data collection schemes in UASNs are significantly different from those in wireless sensor networks due to high power consumption, severe propagation delay, and so on. Furthermore, previous research has overlooked practical conditions, such as characteristics of water delamination and energy constraint on autonomous underwater vehicles (AUVs). In this paper, a stratification-based data collection scheme for three-dimensional UASNs is proposed to solve these problems. In this scheme, the network is divided into two layers on the basis of the Ekman drift current model. The upper layer, called the Ekman layer, suffers large water velocity. Thus, nodes in the upper layer will follow the water flow. In this case, we employ a forward set-based multihop forwarding algorithm for data collection. The lower layer suffers small water velocity so that nodes in this layer are considered as relatively static. A neighbor density clustering-based AUV data gathering algorithm is applied in this layer for data collection. By employing different data collection algorithms in different layers, we can integrate the advantages of a multihop transmission scheme and AUV-aided data collection scheme to reduce network consumption and improve network lifetime. The simulation results also confirm the proposed method has good performance.

In this paper, a high-availability data collection scheme based on multiple autonomous underwater vehicles (AUVs) (HAMA) is proposed to improve the performance of the sensor network and guarantee the high availability of the data collection service. Multi-AUVs move in the network and their trajectory is predefined. The nodes near the trajectory of an AUV directly send their data to the AUV while the others transmit data to nodes that are closer to the trajectory. Malfunction discovery and repair mechanisms are applied to ensure that the network operates appropriately when an AUV fails to communicate with the nodes while collecting data. Compared with existing methods, the proposed HAMA method increases the packet delivery ratio and the network lifetime.

Feature matching is one of the most important steps in the location technology of zooming images. According to the scale-invariant feature transform matching algorithm, several improved false matches elimination algorithms are proposed and compared in this article. First, features of zooming images and ranging models are introduced in detail in the theory framework of the scale-invariant feature transform feature detection and matching algorithm. The key role of the feature matching algorithm and false matches elimination in the ranging technology of zooming images is discussed and addressed. Second, false matches are eliminated by the proposed approach based on geometry constraint in zooming images with a higher accuracy. Third, false matches are removed by an elimination algorithm based on properties of the scale-invariant feature transform features. Finally, an iterative false matches elimination algorithm based on distance from epipole to epipolar line is proposed and this algorithm can also solve the real-time calibration of the shrink-amplify center for zooming images. Experiments results demonstrate that the three false matches elimination algorithms proposed are stable, and the false matches of feature points can be eliminated effectively with combination of these three methods, and the rest matching points can be applied into robot visual servoing.

Energy constraint is a critical issue in the development of wireless sensor networks (WSNs) because sensor nodes are generally powered by batteries. Recently, wireless rechargeable sensor networks (WRSNs), which introduce wireless mobile chargers (MCs) to replenish energy for nodes, have been proposed to resolve the root cause of energy limitations in WSNs. However, existing wireless charging algorithms cannot fully leverage the mobility of MCs because unity between the energy replenishment process and mobile data collection has yet to be realized. Thus, in this paper, a joint energy replenishment and data collection algorithm for WRSNs is proposed. In this algorithm, the network is divided into multiple clusters based on a K-means algorithm. Two MCs visit the anchor point in each cluster by moving along the shortest Hamiltonian cycle in opposite directions. The positions of anchor points are calculated by the base station (BS) based on the energy distribution in each cluster. A spare MC is assigned to the network in case either of the two MCs depletes its energy before reaching the BS. After the two MCs' current tours are over, a semi-Markov model is proposed for energy prediction so anchor points can be updated in the next round. Simulation results demonstrate the semi-Markov-based energy prediction model is highly precise, and the proposed algorithm can replenish energy for network energy effectively.

Wireless rechargeable sensor networks (WRSN) have attracted considerable attention in recent years due to the constant energy supply for battery-powered sensor nodes. However, current technologies only enable the mobile charger to replenish energy for one single node at a time. This method has poor scalability and is not suitable for large-scale WRSNs. Recently, wireless energy transfer technology based on multi-hop energy transfer has made great progress. It provides fundamental support to alleviate the scalability problem. In this paper, the node energy replenishment problem is formulated into an optimization problem. The optimization objective is to minimize the number of non-functional nodes. We propose the uneven cluster-based mobile charging (UCMC) algorithm for WRSNs. An uneven clustering scheme and a novel charging path planning scheme are incorporated in the UCMC algorithm. The simulation results verify that the proposed algorithm can achieve energy balance, reduce the number of dead nodes, and prolong the network lifetime.

Abstract Manipulating the surface structure of electrocatalysts at the atomic level is of primary importance to simultaneously achieve the activity and stability dual‐criteria in oxygen reduction reaction (ORR) for proton exchange membrane fuel cells. Here, a durable acidic ORR electrocatalyst with the “defective‐armored” structure of Pt shell and Pt–Ni core nanoparticle decorated on graphene (Pt–Ni@Pt D /G) using a facile and controllable galvanic replacement reaction to generate gradient distribution of Pt–Ni composition from surface to interior, followed by a partial dealloying approach, leaching the minor nickel atoms on the surface to generate defective Pt skeleton shell, is reported. The Pt–Ni@Pt D /G catalyst shows impressive performance for ORR in acidic (0.1 m HClO 4 ) electrolyte, with a high mass activity of threefold higher than that of Pt/C catalyst owing to the tuned electronic structure of locally concave Pt surface sites through synergetic contributions of Pt–Ni core and defective Pt shell. More importantly, the electrochemically active surface areas still retain 96% after 20 000 potential cycles, attributing to the Pt atomic shell acting as the protective “armor” to prevent interior Ni atoms from further dissolution during the long‐term operation.

Recently, wearable devices have got increasing popularity in wide applications in medical and disaster rescue efforts to ensure the health and safety of users, which facilitates the development of the Internet of Medical Things (IoMT). Due to the posture alteration and mobility of users, the topology of the IoMT changes frequently, which increases the difficulty for resource allocation and routing strategy. In this paper, we respectively probe into the health monitoring architectures of the IoMT for both individual and group, allowing the monitored users to move at will. Furtherly, combined with the diversity of disaster rescuers, we build an IoMT-based disaster rescuer health monitoring system with searchers, doctors and porters. For each application, we point out the enabling technologies and demonstrate the existing researches. It is worth noting that the complexity of environment and high mobility of rescuers increase the probability of route breakage. Thus, this paper creatively addresses effective routing repair solutions for route breakage in IoMT-based disaster rescuer health monitoring system by exploiting the mobility of rescuers. Finally, we forecast three most likely directions in the field of IoMTs.

New criteria for the uniqueness and global robust exponential stability are established for the equilibrium point of interval recurrent neural networks with multiple time-varying delays via a decomposition method and analysis technique. Results are presented in the form of linear matrix inequality, which can be solved efficiently. Two numerical examples are employed to show the effectiveness of the present results.

We investigate eight 1-alkylpyridinium-based ionic liquids of the form [Cn Py][A] by using X-ray photoelectron spectroscopy (XPS). The electronic environment of each element of the ionic liquids is analyzed. In particular, a reliable fitting model is developed for the C 1s region that applies to each of the ionic liquids. This model allows the accurate charge correction of binding energies and the determination of reliable and reproducible binding energies for each ionic liquid. Shake-up/off phenomena are determinedfor both C 1s and N 1s spectra. The electronic interaction between cations and anions is investigated for both simple ionic liquids and an example of an ionic-liquid mixture; the effect of the anion on the electronic environment of the cation is also explored. Throughout the study, a detailed comparison is made between [C8 Py][A] and analogues including 1-octyl-1-methylpyrrolidinium- ([C8 C1 Pyrr][A]), and 1-octyl-3-methylimidazolium- ([C8 C1 Im][A]) based samples, where X is common to all ionic liquids.

Aiming at the problems of the low mobility, low location accuracy, high communication overhead, and high energy consumption of sensor nodes in underwater acoustic sensor networks, the MPL (movement prediction location) algorithm is proposed in this article. The algorithm is divided into two stages: mobile prediction and node location. In the node location phase, a TOA (time of arrival)-based ranging strategy is first proposed to reduce communication overhead and energy consumption. Then, after dimension reduction processing, the grey wolf optimizer (GWO) is used to find the optimal location of the secondary nodes with low location accuracy. Finally, the node location is obtained and the node movement prediction stage is entered. In coastal areas, the tidal phenomenon is the main factor leading to node movement; thus, a more practical node movement model is constructed by combining the tidal model with node stress. Therefore, in the movement prediction stage, the velocity and position of each time point in the prediction window are predicted according to the node movement model, and underwater location is then completed. Finally, the proposed MPL algorithm is simulated and analyzed; the simulation results show that the proposed MPL algorithm has higher localization performance compared with the LSLS, SLMP, and GA-SLMP algorithms. Additionally, the proposed MPL algorithm not only effectively reduces the network communication overhead and energy consumption, but also improves the network location coverage and node location accuracy.

The estimation of the state of charge (SOC) of a battery’s power is one of the key technologies in a battery management system (BMS). As a common SOC estimation method, the traditional ampere-hour integral method regards the actual capacity of the battery, which is constantly changed by the usage conditions and environment, as a constant for calculation, which may cause errors in the results of SOC estimation. Considering the above problems, this paper proposes an improved ampere-hour integral method based on the Long Short-Term Memory (LSTM) network model. The LSTM network model is used to obtain the actual battery capacity variation, replacing the fixed value of battery capacity in the traditional ampere-hour integral method and optimizing the traditional ampere-hour integral method to improve the accuracy of the SOC estimation method. The experimental results show that the errors of the results obtained by the improved ampere-hour integral method for the SOC estimation are all less than 10%, which proves that the proposed design method is feasible and effective.

Polymer-based coatings are a long-established category of protective coatings for metals and alloys regarding corrosion inhibition. The polymer films can degrade, and when coated on metallic substrates, the degradation facilitates moisture and oxygen penetration, reducing the polymer film’s adhesion to the metallic substrate and exposing the substrate to extreme conditions capable of corrosion. For this reason, pigments, inhibitors, and other compatible blends are added to the polymer coating formulations to enhance adhesion and protection. To prevent the possible deterioration of inhibitor-spiked polymer coatings, inhibitors are encapsulated through diverse techniques to avoid leakage and to provide a controlled release in response to the corrosion trigger. This review discusses polymer-based coating performance in corrosion-causing environments to protect metals, focusing more on commercial steels, a readily available construction-relevant material used in extensive applications. It further beams a searchlight on advances made on polymer-based coatings that employ metal–organic frameworks (MOFs) as functional additives. MOFs possess a tailorable structure of metal ions and organic linkers and have a large loading capacity, which is crucial for corrosion inhibitor delivery. Results from reviewed works show that polymer-based coatings provide barrier protection against the ingress of corrosive species and offer the chance to add several functions to coatings, further enhancing their anti-corrosion properties.

In this paper, an autonomous underwater vehicle (AUV) location prediction (ALP)-based data collection scheme (ALP) has been proposed to overcome high and unbalanced energy consumption for underwater wireless sensor networks. In our scheme, an AUV travels around the network, follows a predefined trajectory, and collects data from the sensor nodes. The nodes near the trajectory send their data to the AUV directly, while the others send data to their neighbors that are closer to the trajectory. To overcome the “hot region” problem, which means the nodes near the trajectory of the AUV consume energy faster and die early, a trajectory adjustment mechanism is applied. A mathematical model is proposed to adjust the trajectory periodically. To guarantee an efficient communication between the nodes and the AUV, a reliable time mechanism is proposed. In this mechanism, only the nodes with a sufficient amount of time are capable to communicate and send their data to the AUV directly. The analyses and simulation results validate that our proposed ALP prolongs the network lifetime and has a higher packet delivery ratio than the existing protocols.

A Research of maize disease image recognition of corn leaf based on image processing and analysis, which is to study diseases of image classification. According to the texture characteristics of corn diseases, it uses YCbCr color space technology to segment disease spot, and uses the cooccurrence matrix spatial gray level layer to extract disease spot texture feature, and uses BP neural network to class the maize disease. Application YCbCr color space technology segmented disease spot, and using the co-occurrence matrix spatial gray level layer extracted disease spot texture feature of using BP neural network, on maize disease classification identification. On VC++ platform, do experiments for the study design recognition algorithm, the experimental results show that the algorithm can effectively identify the disease image, the accuracy was as high as 98% or more, the study provided the theoretical basis to cognition of maize leaf disease. the image re of maize leaf disease image recognition to provide a theoretical basis.

Online product reviews play an important role in E-commerce websites because most customers read and rely on them when making purchases. For the sake of profit or reputation, review spammers deliberately write fake reviews to promote or demote target products, some even fraudulently work in groups to try and control the sentiment about a product. To detect such spammer groups, previous work exploits frequent itemset mining (FIM) to generate candidate spammer groups, which can only find tightly coupled groups, i.e. each reviewer in the group reviews every target product. In this paper, we present the loose spammer group detection problem, i.e. each group member is not required to review every target product. We solve this problem using bipartite graph projection. We propose a set of group spam indicators to measure the spamicity of a loose spammer group, and design a novel algorithm to identify highly suspicious loose spammer groups in a divide and conquer manner. Experimental results show that our method not only can find loose spammer groups with high precision and recall, but also can generate more meaningful candidate spammer groups than FIM, thus it can also be used as an alternative preprocessing tool for existing FIM-based approaches.

A heterogeneous ring domain communication topology with equal area in each ring is presented in this paper in an effort to solve the energy balance problem in original IPv6 routing protocol for low power and lossy networks (RPL). A new clustering algorithm and event-driven cluster head rotation mechanism are also proposed based on this topology. The clustering information announcement message and clustering acknowledgment message were designed according to RFC and original RPL message structure. An energy-efficient heterogeneous ring clustering (E2HRC) routing protocol for wireless sensor networks is then proposed and the corresponding routing algorithms and maintenance methods are established. Related messages are analyzed in detail. Experimental results show that in comparison against the original RPL, the E2HRC routing protocol more effectively balances wireless sensor network energy consumption, thus decreasing both node energy consumption and the number of control messages.

In this work, the influence of rolling ratios on microstructural changes and corrosion behavior of an as-rolled Mg-8 wt.%Li alloy in 0.1 mol/L NaCl solution has been investigated. It revealed that with the rolling ratio being increased from 3 to 10, the α-Mg phases were elongated and fragmented, whilst the area fraction of exposed β-Li phases increased. Meanwhile, the corrosion performance of the alloy decreased with the increased rolling ratios. For all the samples, their corrosion processes were quite similar and can have two stages. At the initial stage with the samples being immersed for less than 6 h, the corrosion mainly occurred in β-Li phases. When the samples were immersed for longer than 6 h, the corrosion attack transferred to α-Mg phases and the hydrogen evolution rate was accelerated.