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In the ultrasonic testing of the mechanical properties of ferromagnetic materials, the main excitation mechanism modes of ultrasonic waves are Lorentz force mechanism ultrasonic and magnetostriction mechanism ultrasonic. Lorentz force mechanism ultrasonic mainly detects the conductive characteristics of materials, while magnetostriction mechanism ultrasonic mainly detects the hysteresis characteristics of materials. However, conventional electromagnetic acoustic transducers (EMATs) cannot simultaneously characterise these two characteristics of ferromagnetic materials. In this study, we conclude from the finite element simulation that a quantitative lift-off distance (i.e. the distance between EMAT and the specimen) can change the main mechanism mode of the excited ultrasonic wave. Based on this conclusion, an electromagnet electromagnetic acoustic transducer with dual main excitation modes (DM-E-EMAT) is designed, which can realise the dual mode switching of the main Lorentz force mechanism and main magnetostriction mechanism by adding or removing hollow square gasket. The ultrasonic testing of ferromagnetic materials based on the two main mechanisms of DM-E-EMAT can simultaneously characterise the conductivity and magnetostriction properties of ferromagnetic materials, which enables the characterisations of two types of characteristic parameters with one transducer, improves the efficiency of electromagnetic ultrasonic non-destructive testing of the mechanical properties and reduces the detection errors of ferromagnetic material. [ABSTRACT FROM AUTHOR]

Future communication networksrequire higher bandwidth, greater coverage, and better throughput. The Space-Air-Ground Integrated Network (SAGIN) has the advantage of wide-area coverage and can cover global communications. It can meet the needs of network resources for maritime activities and remote mountainous areas, which is of great significance to the realization of a new generation of communications networks. With the development of the network and the emergence of delay-sensitive applications such as the Internet of Things, improving the delay performance of the system has received extensive attention. SAGIN involves multiple networks and is more complex than other networks. If there is no reasonable management between different networks, it is easy to lead to difficult link deployment and high time delay. On the basis of research and customization of SFC technology, it can provide a wide range of services and other advantages. It has significant performance in application scenarios involving multi network integration. In order to solve the above problems, this paper studies the SAGIN architecture of SFC based on business types. A service function chain mapping method based on delays prediction is proposed. Calculate the delay of the deployment path and select the path with the lowest delay as the SFC mapping path. The service model is constructed according to the mapping path, and the network slices are divided based on the service type. The simulation results show that the SFC mapping algorithm based on time delay prediction is compared with the traditional SFC mapping scheme. The algorithm does not affect other indicators, the CPU resource utilization rate is 27.8 $\%$ higher, and the link resource utilization rate is 22.7 $\%$ higher. The service acceptance rate increased by 21.5 $\%$ , the latency performance increased by 38.2 $\%$ , and the total resource consumption is reduced by 25.2 $\%$. [ABSTRACT FROM AUTHOR]

The coordinated development of big data, Internet of Things, cloud computing and other technologies has led to an exponential growth in Internet business. However, the traditional Internet architecture gradually shows a rigid phenomenon due to the binding of the network structure and the hardware. In a high-traffic environment, it has been insufficient to meet people's increasing service quality requirements. Network virtualization is considered to be an effective method to solve the rigidity of the Internet. Among them, virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a large number of research has focused on the evolutionary algorithm's masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and its low flexibility makes it unable to adapt to increasingly complex network environments. In addition, link-mapping strategies that do not consider load balancing can easily cause link blocking in high-traffic environments. In the IoT environment involving medical, disaster relief, life support and other equipment, network performance and stability are particularly important. Therefore, how to provide a more flexible virtual network mapping service in a heterogeneous network environment with large traffic is an urgent problem. Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed. This strategy uses a dynamically calculated cross-probability and pheromone-based mutation gene selection strategy to improve the flexibility of the algorithm. In addition, a weight update mechanism based on load balancing is introduced to reduce the probability of mapping failure while balancing the load. Simulation results show that the proposed method performs well in a number of performance metrics including mapping average quotation, link load balancing, mapping cost-benefit ratio, acceptance rate and running time. [ABSTRACT FROM AUTHOR]

The existing research has explored the effects of growth mindset intervention on individuals in Western culture. This study sought to determine whether growth mindset intervention has a positive impact on adolescents in economically disadvantaged areas of China. Participants in this study were 324 junior high school students who were randomly divided into the intervention group and the control group. The intervention group received six weeks of intervention classes designed to help students learn, internalize, and reinforce the concept of growth mindset. The aims of intervention were to build students' beliefs that the brain is plastic and that individuals can change by their efforts and help students acquire the strategies to cope with the difficulties. The control group was given six classes on mental health, including time management, habit formation, and memory strategies, which were unrelated to growth mindset. All participants' implicit theory of intelligence, fixed-trait attributions, grit, and state anxiety were assessed in the pre-test and post-test. The results showed that compared with the control group, the intervention group had a significant increase in growth mindset, the level of grit, and decrease in fixed-trait attributions. That is, for students in the intervention group, strengthening of growth mindset was accompanied by more frequent use of process-focused attribution styles, more perseverance, and greater efforts when faced with challenges and setbacks. Collectively, the results suggested that having a strong growth mindset of intelligence may help students adopt more proactive coping strategies and protect them from the deleterious effects of poverty on student development. [ABSTRACT FROM AUTHOR]

With the development of science and technology and the need for multi-criteria decision making (MCDM), the optimization problem to be solved becomes extremely complex. The theoretically accurate and optimal solutions are often difficult to obtain. Therefore, meta-heuristic algorithms based on multi-point search have received extensive attention. The flower pollination algorithm (FPA) is a new swarm intelligence meta-heuristic algorithm, which can effectively control the balance between global search and local search through a handover probability, and gradually attracts the attention of researchers. However, the algorithm still has problems that are common to optimization algorithms. For example, the global search operation guided by the optimal solution is easy to lead the algorithm into local optimum, and the vector-guided search process is not suitable for solving some problems in discrete space. Moreover, the algorithm does not consider dynamic multi-criteria decision problems well. Aiming at these problems, the design strategy of hybrid flower pollination algorithm for virtual network embedding problem is discussed. Combining the advantages of the genetic algorithm and FPA, the algorithm is optimized for the characteristics of discrete optimization problems. The cross-operation is used to replace the cross-pollination operation to complete the global search and replace the mutation operation with self-pollination operation to enhance the ability of local search. Moreover, a life cycle mechanism is introduced as a complement to the traditional fitness-based selection strategy to avoid premature convergence. A chaos optimization strategy is introduced to replace the random sequence-guided crossover process to strengthen the global search capability and reduce the probability of producing invalid individuals. In addition, a two-layer BP neural network is introduced to replace the traditional objective function to strengthen the dynamic MCDM ability. Simulation results show that the proposed method has good performance in link load balancing, revenue–cost ratio, VN requests acceptance ratio, mapping average quotation, average time delay, average packet loss rate, and the average running time of the algorithm. [ABSTRACT FROM AUTHOR]

The outbreak of coronavirus COVID-19 not only brings great disaster to the people of the world, but also brings heavy burden to the medical and health network system. Massive network data traffic and resource optimization requests make traditional network architectures unable to calmly deal with the impact of COVID-19. Artificial intelligence (AI) can effectively raise the upper limit of the medical and health network, as evidenced by the ever-increasing restorative clinical data. In addition, the development of next-generation network (NGN) technologies based on machine learning (ML) has created unlimited possibilities for the emergence of emerging medical methods. In order to reflect the effective results of the current application of AI technologies in the fight against the COVID-19 epidemic and provide a reliable guarantee for subsequent diagnosis and treatment of COVID-19 epidemics, a series of AI technologies which can be used in the diagnosis and treatment of COVID-19 are systematically summarized and analyzed. Based on various AI technologies and methods, we try to propose an AI-based medical network architecture. The architecture uses AI technologies to quickly and effectively realize the monitoring, diagnosis and treatment of patients. Finally, we rationally analyzed the technical challenges and practical problems that may be faced in implementing the architecture. The purpose of this article is to inspire scholars and medical researchers to carry out the latest research in response to the COVID-19 epidemic and make breakthrough medical technology progress. [ABSTRACT FROM AUTHOR]

Wireless network communication has developed rapidly in recent years, especially in the field of Internet of vehicles (IoV). However, due to the limitations of traditional network architecture, resource scheduling in wireless network environment is still facing great challenges. We focus on the urgent need of users for bandwidth resources in the IoV scenario under virtual network environment. This paper proposes a bandwidth aware multi domain virtual network embedding (BA-VNE) algorithm. The algorithm is mainly aimed at the problem that users need a lot of bandwidth in wireless communication mode, and solves the problem of bandwidth resource allocation from the perspective of virtual network embedding (VNE). In order to improve the performance of the algorithm, we introduce particle swarm optimization (PSO) algorithm to optimize the performance of the algorithm. In order to verify the effectiveness of the algorithm, we have carried out simulation experiments from link bandwidth, mapping cost and virtual network request (VNR) acceptance rate. The final results show that the proposed algorithm is better than other representative algorithms in the above indicators. [ABSTRACT FROM AUTHOR]

Network virtualization is an effective manner to address the ossification issue of Internet architecture. Virtual network embedding is one of the most critical techniques in network virtualization environments. Several security problems about virtual network embedding are introduced due to the fact that virtual network embedding adds a virtual layer into the internet architecture. In this paper, we proposed an approach for security aware virtual network embedding called SA-VNE to address the security problems in virtual network embedding process. Firstly, we use the information entropy TOPSIS method to rank the importance of substrate nodes with an aim to choose the most appropriate substrate node for accommodating the virtual node. Secondly, we use the shortest path algorithm to perform the link mapping process. Simulation results demonstrated that our proposed SA-VNE algorithm behaves better that those of state-of-the-art existing security aware virtual network embedding algorithms in terms of the long-term average revenue, the long-term average VN acceptance ratio, the long-term average revenue to cost ratio and the running time. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the quality of clinical practice guidelines(CPGs) for patients with diabetic foot worldwide. A search of guidelines websites, databases and academic institutions websites was performed from January 1st, 2010, until June 30th, 2018. Four assessors independently rated the quality of each CPG using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument. Twelve CPGs satisfied the inclusion criteria. The median scores for the 6 AGREE II domains (scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence) were 92.5%, 72.5%, 71.5%, 89%, 47%, and 77%, respectively. The overall quality of the CPGs was good since the majority of the CPGs reached an overall guideline quality between 5 and 7 points. Different CPGs had widely varying scores in the same area, ranging from 25 to 94 points. [ABSTRACT FROM AUTHOR]

• SHMP-corn starch aerogels (CSA) were prepared to encapsulate β-carotene. • Increased relative crystallinity and decreased short-range molecular order demonstrated the successful encapsulation of β-carotene. • Almost 80% of β-carotene was released in simulated in vitro release procedure. • SHMP-CSA can effectively protect β-carotene under ultraviolet and light irradiation. This study aimed to prepare a starch-based aerogel with microporous network structure, and to investigate its physicochemical properties after β-carotene encapsulation. Corn starch aerogels (CSA) prepared with sodium hexametaphosphate (SHMP) as a cross-linking agent and β-carotene encapsulation were evaluated in terms of morphology, long- and short-range molecular order, bioavailability, and stability. After encapsulating β-carotene, the morphology of SHMP-CSA showed that the aerogels presented agglomeration, and the relative crystallinity increased from 17.2% to 22.2%. The characteristic bands of β-carotene were not found in the FT-IR pattern, and the short-range molecular order of aerogel was decreased, proving that β-carotene was well embedded in the aerogel. During the simulated in vitro release process, β-carotene was almost completely released. After ultraviolet or light irradiation, the retention rate of β-carotene was much higher than that in the control group. These results demonstrated that SHMP-CSA encapsulation could effectively improve the stability of β-carotene. [ABSTRACT FROM AUTHOR]

Normal growth of granulosa cells (GCs) is essential for follicular development. miR-200b plays a vital role in litter size, estrous cycle, ovulation, and follicular development in sheep. However, it is unclear that the specific effect and regulatory mechanism of miR-200b on ovine GCs. miR-200b mimic inhibited GCs proliferation and induced cellular senescence through downregulating mitochondrial membrane potential (MMP), concentration of ATP and mitochondrial respiratory chain complex Ⅰ, and upregulating SA-β-gal positive rate and ROS production. A total of 597 differentially expressed genes were identified by RNA-Seq in GCs transfected with miR-200b mimic and mimic NC, and they were involved in cell cycle and cellular senescence. miR-200b directly targeted and downregulated MYBL2 and CDK1. Overexpression of MYBL2 promoted GCs proliferation and genes expression (CDK1 , CDC20 , MAD2L1 and FOXM1), which were suppressed by miR-200b mimic. Furthermore, MYBL2 negatively regulated miR-200b-induced GC senescence. In conclusion, miR-200b/MYBL2/CDK1 regulated proliferation and senescence through cell cycle pathway in ovine granulosa cells. Our study provides a novel insight that miR-200b regulates ovine follicular development. • miR-200b inhibits proliferation and induces cellular senescence of ovine granulosa cells. • miR-200b is involved in cell cycle and cellular senescence ovine granulosa cells through RNA-seq. • miR-200b directly targets MYBL2 and CDK1 by dual luciferase reporter assay. • miR-200b/MYBL2/CDK1 suppresses proliferation and induces senescence through cell cycle arrest in ovine GCs. [ABSTRACT FROM AUTHOR]

In the intelligent transportation system (ITS), the vehicular fog computing network (VFCN) can effectively alleviate the bottleneck existing in the cloud computing framework, such as high latency-sensitive applications, through edge computing offloading. It uses vehicles as the infrastructure, and fog nodes can communicate, perceive and share resources, so resource orchestration has become an essential issue of VFCN. To reduce the communication transportation cost and improve the resource utilization of VFCN, we propose a spectral graph theory-based resource orchestration algorithm by combining Virtual Network Embedding (VNE) and Deep Reinforcement Learning (DRL). Specifically, we propose a four-layer strategy network based on Graph Convolutional Networks (GCNs) for computing node embedding probability, where fog nodes fully mine spatial structure information by fusing themselves with neighborhood information to compensate for the lack of traditional heuristic VNE. Moreover, fog link embedding is performed by breadth-first search (BFS). Finally, the effectiveness of the proposed strategy is scientifically and rigorously proved in terms of long-term average revenue, long-term average revenue-cost ratio, and VNR acceptance rate through simulation cases, which can reasonably arrange the resources of VFCN. [ABSTRACT FROM AUTHOR]

In network virtualization, one of its core challenges lies in how to map the virtual networks (VNs) to the shared substrate network (SN) that is managed by an infrastructure provider, termed as the virtual network embedding problem. Prior studies on this problem only consider one objective, e.g., maximizing the revenues by mapping more VNs or minimizing the energy cost. In this paper, we addressed the virtual network embedding problem with these two objectives. We leverage niche particle swarm optimization technique to design a meta-heuristic algorithm to solve this problem. Extensive simulations illustrate that the efficiency of our proposed algorithm is better than the state-of-the-art algorithms in terms of both revenue and energy cost. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) separates the control layer from the data layer, and decisions to manage the network are issued through a controller. The distributed SDN architecture is an effective solution addressing modern SDN architectures and allows multiple controllers to manage different parts of the network to ensure efficient and stable operation. To solve the problems of high switch migration cost, load imbalance, and inefficient load balancing in SDN multi-controller environments, we propose a deep learning-based controller load prediction switch migration strategy. This strategy uses a migration switch selection algorithm, a target controller selection algorithm, and a switch migration decision algorithm. Then, we propose a load balancing algorithm based on this decision algorithm. The final experimental results show that the load prediction switch migration strategy reduces the migration cost by 16% and 8%, respectively, compared with time-sharing switch migration and distributed decision migration strategies, reduces load variance from 0.02 to 0.004 compared with the distributed decision migration strategy, and improves load balancing efficiency by 27.6% compared with the time-sharing switch migration strategy. [ABSTRACT FROM AUTHOR]

In this letter, a comprehensive and detailed introduction to the current security risks faced by networked printers is given, the security monitoring platform and attack detection method are explained, and the actual monitoring results are analyzed. In particular, the most comprehensive feature knowledge database for networked printer is organized and published. [ABSTRACT FROM AUTHOR]

The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements. [ABSTRACT FROM AUTHOR]

Granulosa cells (GCs) proliferation and apoptosis play a significantly role in follicular development and atresia. ID1 and miR-150 are involved in cell apoptosis and follicular atresia, but the interaction and function of ID1 and miR-150 in GCs are unclear. This study focuses on ID1 and miR-150 in terms of the interaction and effects on proliferation and apoptosis in ovine granulosa cells. Our findings revealed that ID1 decreased the promoter activity and expression level of oar-miR-150. However, the expression of ID1 was downregulated by miR-150, and ID1 was identified as a target gene of oar-miR-150. miR-150 mimic inhibited proliferation and upregulated apoptosis rate in ovine GCs, while the results of miR-150 inhibitor were opposite. Overexpression of ID1 significantly inhibited ovine GCs proliferation and cell cycle-related genes (CDK1 , CDK2 , CDK4 , CCND2 , CDC20 , and PCNA) expression, whereas knockdown of ID1 promoted cell proliferation and those genes expression. Overexpression of ID1 significantly downregulated mitochondrial membrane potential and Bcl-2 expression in ovine GCs, and upregulated the expression of pro-apoptosis genes Bax , Caspase-3 , and Caspase-9 , whereas the results of ID1 knockdown were reversed. Collectively, these findings indicate the interaction and the vital role of ID1 and miR-150 on proliferation and apoptosis in ovine granulosa cells, which may suggest a novel target for ovine follicular development and atresia. • ID1, as a transcription factor, represses the transcription of miR-150 in ovine granulosa cells. • ID1 , as a mRNA, is a target gene of oar-miR-150. • ID1 inhibits proliferation and promotes apoptosis of ovine granulosa cells. [ABSTRACT FROM AUTHOR]

The transition towards full network virtualization will see services for smart ecosystems including smart metering, healthcare and transportation among others, being deployed as Service Function Chains (SFCs) comprised of an ordered set of virtual network functions. However, since such services are usually deployed in remote cloud networks, the SFCs may transcend multiple domains belonging to different Infrastructure Providers (InPs), possibly with differing policies regarding billing and Quality-of-service (QoS) guarantees. Therefore, efficiently allocating the exhaustible network resources to the different SFCs while meeting the stringent requirements of the services such as delay and QoS among others, remains a complex challenge, especially under limited information disclosure by the InPs. In this work, we formulate the SFC deployment problem across multiple domains focusing on delay constraints, and propose a framework for SFC orchestration which adheres to the privacy requirements of the InPs. Then, we propose a reinforcement learning (RL)-based algorithm for partitioning the SFC request across the different InPs while considering service reliability across the participating InPs. Such RL-based algorithms have the intelligence to infer undisclosed InP information from historical data obtained from past experiences. Simulation results, considering both online and offline scenarios, reveal that the proposed algorithm results in up to 10% improvement in terms of acceptance ratio and provisioning cost compared to the benchmark algorithms, with up to more than 90% saving in execution time for large networks. In addition, the paper proposes an enhancement to a state-of-the-art algorithm which results in up to 5% improvement in terms of provisioning cost. [ABSTRACT FROM AUTHOR]

The design of an intrusion detection system (IDS) plays a critical role in guaranteeing the security of the Industrial Internet of Things (IIoT). Recently, the rapid development of edge-based IIoT has posed new challenges in the design of a beneficial IDS considering its billions of IIoT devices and substantially decentralized data interaction. Both the detection method and the system architecture become the key components in constructing an efficient IDS for edge-based IIoT. In this article, we survey the typical state-of-theart studies about detection methods and system structure of IDS. Moreover, we propose a hybrid IDS architecture and introduce a machine learning aided detection method, which outperforms the literature in terms of a range of benchmarks. [ABSTRACT FROM AUTHOR]

Due to the rapid development of network techniques, packet-switched systems experience high-speed growth of traffic, which imposes a heavy and unbalanced burden on the routers. Hence, efficient routing schemes are required in order to achieve load balance. By decoupling the control plane and the data plane, Software-Defined Network (SDN) shows its flexibility and extensibility to achieve the automatic management of network resources. Based on the SDN architecture, we propose a pair of machine learning aided load balance routing schemes considering the queue utilization (QU), which divide the routing process into three steps, namely the dimension reduction, the QU prediction, as well as the load balance routing. To the best of our knowledge, it is the first time that principal component analysis (PCA) is used for the dimension reduction of the substrate network. Furthermore, QU prediction is conducted with the aid of neural network algorithms for the sake of coping with the network congestion resulting from burst traffic. Finally, simulation results show that our proposed routing schemes considering QU predicted by the machine learning algorithms outperform the traditional Bellman–Ford (BF) routing strategy in terms of the average packet loss ratio, the worst throughput and the average delay. [ABSTRACT FROM AUTHOR]

In recent years, hyperspectral image classification techniques have attracted a lot of attention from many scholars because they can be used to model the development of different cities and provide a reference for urban planning and construction. However, due to the difficulty in obtaining hyperspectral images, only a limited number of pixels can be used as training samples. Therefore, how to adequately extract and utilize the spatial and spectral information of hyperspectral images with limited training samples has become a difficult problem. To address this issue, we propose a hyperspectral image classification method based on dense pyramidal convolution and multi-feature fusion (DPCMF). In this approach, two branches are designed to extract spatial and spectral features, respectively. In the spatial branch, dense pyramid convolutions and non-local blocks are used to extract multi-scale local and global spatial features in image samples, which are then fused to obtain spatial features. In the spectral branch, dense pyramidal convolution layers are used to extract spectral features in image samples. Finally, the spatial and spectral features are fused and fed into fully connected layers to obtain classification results. The experimental results show that the overall accuracy (OA) of the method proposed in this paper is 96.74%, 98.10%, 98.92% and 96.67% on the four hyperspectral datasets, respectively. Significant improvements are achieved compared to the five methods of SVM, SSRN, FDSSC, DBMA and DBDA for hyperspectral classification. Therefore, the proposed method can better extract and exploit the spatial and spectral information in image samples when the number of training samples is limited. Provide more realistic and intuitive terrain and environmental conditions for urban planning, design, construction and management. [ABSTRACT FROM AUTHOR]

As an important step in natural language processing (NLP), text classification system has been widely used in many fields, like spam filtering, news classification, and web page detection. Vector space model (VSM) is generally used to extract feature vectors for representing texts which is very important for text classification. In this paper, a feature selection algorithm based on synonym merging named SM-CHI is proposed. Besides, the improved CHI formula and synonym merging are used to select feature words so that the accuracy of classification can be improved and the feature dimension can be reduced. In addition, for feature words selected by SM-CHI, this paper presented three weight calculation algorithms to explore the best feature weight update method. Finally, we designed three comparative experiments and proved the classification accuracy is the highest when choosing the improved CHI formula 2, set the threshold α to 0.8 and use the largest weight among the synonyms to update the feature weight, respectively. [ABSTRACT FROM AUTHOR]

A pH/NIR responsive and injectable hydrogel was constructed by combining three biocompatible components—CaCO3, polydopamine and sodium alginate—for local calcium ions interference therapy and photothermal therapy against melanoma. [Display omitted] Melanoma is an aggressive tumor located in skin with high rates of recurrence and metastasis. Due to the limited traditional therapies, the development of novel strategies against melanoma is urgently quested. To reduce the side effects of traditional administration ways and amplify the killing effect, an injectable sodium alginate (SA)-based hydrogels were developed, in which CaCO 3 /polydopamine nanoparticles (CaCO 3 /PDA NPs) were embedded for the synergistic photothermal/calcium ions interference therapy of melanoma. In the study, the formation conditions and mechanical properties of CaCO 3 /PDA-SA hydrogels were characterized, and their antitumor efficiency and mechanism against mouse melanoma cells were investigated. When injected intratumorally, CaCO 3 /PDA-SA fluid was converted into hydrogel in situ through the interaction of pH-sensitive released Ca2+ and alginate chains, which increased the retention time of photothermal agents (CaCO 3 /PDA NPs) at tumor sites and thereby was more conducive to produce hyperthermia via photothermal conversion to combat melanoma. Moreover, in acidic tumor microenvironment, the residual CaCO 3 /PDA NPs in hydrogels continuously decomposed and released Ca2+ to destroy the Ca2+ buffering capacity and evoke the mitochondrial Ca2+-overloading, resulting in the inhibition of adenosine triphosphate production to accelerate cell death. Notably, besides the heat elevation, the near-infrared light (NIR) irradiation would further enhance the release of Ca2+ to promote the Ca2+-involved cell death. Therefore, a pH/NIR-responsive and injectable SA-based hydrogels were successfully established and showed enhanced treatment efficacy of melanoma through the synergism of photothermal therapy and calcium ions interference therapy. [ABSTRACT FROM AUTHOR]

Objective: To explore hsCRP and ET-1 expressions in patients with no-reflow phenomenon after percutaneous coronary intervention (PCI). Methods: A total of 136 patients with single coronary artery disease receiving PCI were divided into a reflow group and a no-reflow group to compare the level use of ET-1 alone with combined level of ET-1 and hs-CRP in PCI regarding sensitivity, specificity, positive and negative predictive values and accuracy for postoperative no-reflow. The study was conducted between 2014-2016 at our hospital. Results: Postoperative levels of ET-1 and hs-CRP in no-reflow group were significantly higher than those of reflow group (P<0.05). ET-1 level of reflow group peaked three hours after PCI and then declined. Serum level of hs-CRP decreased most obviously within three hours after PCI in reflow group and three hours - three days after PCI in no-reflow group. Left ventricular end-diastolic diameters of both groups after PCI were apparently lower than those before PCI, without significant inter-group difference (P>0.05). Left ventricular end-systolic diameters and left ventricular ejection fractions of both groups evidently increased after PCI, without significant inter-group differences either (P>0.05). Corrected TIMI frame count (CTFC) and wall motion score index of reflow group after PCI were significantly lower than those of no-reflow group (P<0.05). ET-1 level was positively correlated with CTFC (P<0.05). Multivariate linear regression showed hs-CRP was negatively correlated with the serum level (P<0.05) (r=-0.34). Conclusion: hsCRP and ET-1 levels significantly increased in patients with no-reflow phenomenon. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Load balancing strategy for SDN multi-controller clusters based on load prediction" published in the Journal of Supercomputing. The correction notice provides the correct affiliations for the author Laith Abualigah, who is affiliated with various institutions in Jordan and Malaysia. The correction notice is signed by Junbi Xiao, Xingjian Pan, Jianhang Liu, Jian Wang, Peiying Zhang, and Laith Abualigah. Springer Nature, the publisher, remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

• One of the first attempts of use GATs to solve cross-modal hashing issues. • Pay more attention to the most important information to enhance the robustness. • Design an label encoder to guide the feature extraction to narrow the modality gap. • Use multi-label annotations to bridge the semantic relevance at a fine-grained level. • MS 2 GAH can achieve satisfactory performance. Due to the strong nonlinear representation capabilities of deep neural networks and the low storage and high efficiency characteristics of hash learning, deep cross-modal hashing has been propelled to the forefront of academics. How to preferably bridge semantic relevance to further bridge the semantic modality gap is the vital bottleneck to improve model performance. Confronting samples with rich semantics, how to comprehensively explore the hidden correlations and establish more precise modality relationships is the primary issue to be solved. In this work, we propose a novel deep hashing method called M ulti-Label S emantic S upervised G raph A ttention H ashing (MS 2 GAH), which is an end-to-end framework that integrates graph attention networks (GATs). It constructs graph features through the adjacency of nodes and assigns different weights to adjacent edges to enhance the robustness of the model. Simultaneously, multi-label annotations are utilized to bridge the semantic relevance between modalities in a more fine-grained manner. To make preferable use of rich semantic information, an end-to-end label encoder is designed to mine high-level semantics from multi-label annotations to guide the feature extraction process of specific-modality networks, thereby further narrowing the modality gap. Finally, extensive experiments have been conducted on four datasets, and the results show that MS 2 GAH is superior to other baselines and one step forward. [ABSTRACT FROM AUTHOR]

Aims and objectives: To compare the safety of replacing peripheral intravenous catheter as clinically indicated versus routine replacement on patient outcomes in the Chinese context. Background: Some evidence from developed countries recommend replacing peripheral intravenous catheter as clinically indicated; however, there is limited evidence from developing countries. Design: A multisite randomised controlled trial. Methods: The 3050 participants from three hospitals in China were randomly assigned to clinically indicated or routine replacement groups. Patients in the clinically indicated group had the catheters kept in situ until any of the following clinical signs appeared: phlebitis, infiltration, occlusion, displacement, local infection and diagnosed catheter‐related bloodstream infection. Patients in the routine replacement group had their peripheral intravenous catheters replaced every 96 hours. The outcomes of phlebitis, infiltration, occlusion, displacement; catheter‐related bloodstream infection, all‐cause bloodstream infection, and local infection were compared. CONSORT checklist was used to guide the reporting of this RCT. Results: The risk of phlebitis, phlebitis per 1000 catheter days, occlusion, dislodgement, all bloodstream infections, local infection and mortality between the two groups were not significantly different. The risk of infiltration was increased in the clinically indicated group (HR 1.29). There was no catheter‐related bloodstream infection reported in either group. Patients' first peripheral intravenous catheter dwelling time and cumulative indwelling time of all peripheral intravenous catheters in the clinically indicated group were significantly longer than the routine replacement group. There was no statistical significant difference in survival times from phlebitis between the two groups. Conclusions: In the Chinese context, removing peripheral catheters as clinical indicated did not increase the risk of phlebitis, occlusion, catheter displacement and catheter infection; however, there was an increased infiltration incidence. Relevance to clinical practice: In developing countries, removing peripheral catheters as clinical indicated is feasible, but more frequent observations of infiltration are highly recommended. [ABSTRACT FROM AUTHOR]

Summary It is expected that the demand for quality of service (QoS) and quality of experience (QoE) in future 6G scenarios will continue to increase, and edge computing (EC) will continue to receive widespread attention. However, the highly dynamic and connectivity complexity of edge computing networks (ECNs) pose severe challenges to its resource allocation issues. In addition, network virtualization (NV) is widely applied to increase the flexibility of network architectures. Based on the above inspirations, we adopt virtual network embedding (VNE) to make decisions on the resource allocation of ECN. Specifically, we deploy a deep reinforcement learning (DRL)‐based multi‐layer policy network, which is applied to extract environmental information, calculate the available resources of edge nodes, and screen candidate edge nodes that satisfy resource allocation conditions. Second, the resources of edge links are allocated according to the shortest path algorithm. Finally, we build a simulation environment to demonstrate the advantages of the proposed policy network through rich experiments. [ABSTRACT FROM AUTHOR]

With the development of transportation and the ever-improving of vehicular technology, Artificial Intelligence (AI) has been popularized in Intelligent Transportation Systems (ITS), especially in Traffic Flow Prediction (TFP). TFP plays an increasingly important role in alleviating traffic pressure caused by regional emergencies and coordinating resource allocation in advance to deployment decisions. However, existing research can hardly model the original intricate structural relationships of the transportation network (TN) due to the lack of in-depth consideration of the dynamic relevance of spatial, temporal, and periodic characteristics. Motivated by this and combined with deep learning (DL), we propose a novel framework entitled Fully Dynamic Self-Attention Spatio-Temporal Graph Networks (FDSA-STG) by improving the attention mechanism using Graph Attention Networks (GATs). In particular, to dynamically integrate the correlations of spatial dimension, time dimension, and periodic characteristics for highly-accurate prediction, we correspondingly devised three components including the spatial graph attention component (SGAT), the temporal graph attention component (TGAT), and the fusion layer. In this case, three groups of similar structures are designed to extract the flow characteristics of recent periodicity, daily periodicity, and weekly periodicity. Extensive evaluation results show the superiority of FDSA-STG from perspectives of prediction accuracy and prediction stability improvements, which also testifies high model adaptability to the dynamic characteristics of the actual observed traffic flow (TF). [ABSTRACT FROM AUTHOR]

This paper investigates the multi-part collaborative task offloading with multiple servers in mobile edge computing (MEC) systems by considering server overload and long-term system performance. Our goal is to achieve the optimal user experience within the range of the network operator’s affordable cost. To this end, we design a two-layer computation offloading framework based on the multi-part offloading mode and the collaborations among small cell base station (SBS) servers. Furthermore, a multi-object constrained optimization problem is formulated, which jointly optimizes user association, channel allocation, and multi-part collaborative task offloading. To solve this problem, we propose a Genetic and Deep Deterministic Policy Gradient (GADDPG)-based computation offloading scheme, where the long-term system performance is considered. Numerical and simulation results demonstrate that the proposed GADDPG-based computation offloading scheme outperforms other methods in effectively solving the server overload and guarantees long-term system performances while ensuring the best user experience. [ABSTRACT FROM AUTHOR]

• This proposed mode decomposition algorithm recovers the mode coefficients of two orthogonal polarizations through measurements of intensity distribution of three polarization angles. • The relationship between the field intensity distribution and mode coefficients is derived for the polarization angle of 45 °. • Additionally, an objective function is introduced to select the optimal solution and mitigate the phase ambiguity. • For simulation results, the mean value of amplitude and phase errors is found to be less than 10-6, with almost 100% correlation between the reconstructed and the original mode. For experimental results, the correlation value is greater than 95%. A mode decomposition algorithm for spatially degenerated modes within few-mode optical fibers is proposed. This proposed algorithm recovers the mode coefficients of two orthogonal polarizations through measurements of intensity distribution of three polarization angles. The relationship between the field intensity distribution and mode coefficients is derived for the polarization angle of 45 °. Additionally, an objective function is introduced to select the optimal solution and mitigate the phase ambiguity. Through multiple decompositions, the mean value of amplitude and phase errors is found to be less than 10-6, with almost 100% correlation between the reconstructed and the original mode. The experimental results also show that the algorithm can restore the field intensity distribution from the captured images. [ABSTRACT FROM AUTHOR]

With the advancement of wireless communication technology, the number of wireless network terminals has exploded, and various new business scenarios have emerged. The 6G mobile communication technology not only surpasses 5G standards in terms of transmission rate, delay, power and other performances, but also extends the communication range to multiple fields such as air, ground, ocean, etc., which greatly promotes Unmanned Aerial Vehicle (UAV) communication technology research and development. Compared to terrestrial networks, UAV communication has advantages such as high flexibility and easy deployment. However, there are still many problems and challenges in practical applications. In this paper, we will first introduce the functions and application scenarios of UAV communication, then discuss the current challenges and related technical research, and finally look forward to the future development prospects. [ABSTRACT FROM AUTHOR]

Using lexical judgment tasks, the present study explored whether perspective taking affected attention bias to body-related information among junior high school students with body image disturbance. Experiment 1 examined the junior high school students' attention bias to body schema-related words; the results showed the body image disturbance group responded significantly more quickly to negative body schema-related words than positive words, whereas the control group did not show a significant difference between positive and negative words. In Experiment 2, participants were asked to judge whether the positive or negative body schema-related words were suitable to describe themselves, when adopting their own perspective or that of another person. The results showed that reaction times to negative words were significantly shorter than to positive words when adopting a self-perspective. When taking another's perspective, there was no significant difference of reaction time between positive and negative words. This result demonstrated that perspective taking reduced attention bias to negative body schema-related information among junior high school students with body image disturbance. The present research suggests that guiding adolescents to view themselves from different perspectives can help them form a more accurate and objective body image. • Attention bias among junior high school students with body image disturbance is examined. • Lexical judgment tests reveal attention bias to negative body-related information. • Perspective taking reduces attention bias. • Implications for helping adolescents with negative body schemata are discussed. [ABSTRACT FROM AUTHOR]

With the dramatic opening-up of network, network security becomes a severe social problem with the rapid development of network technology. Intrusion Detection System (IDS) is an innovative and proactive network security technology, which becomes a hot topic in both industry and academia in recent years. There are four main characteristics of intrusion data that affect the performance of IDS including multicomponent, data imbalance, time-varying and unknown attacks. We propose a novel IDS framework called HMLD to address these issues, which is an exquisite designed framework based on Hybrid Multi-Level Data Mining. In this paper, we use KDDCUP99 dataset to evaluate the performance of HMLD. The experimental results show that HMLD can reach 96.70% accuracy which is nearly 1% higher than the recent proposed optimal algorithm SVM+ELM+Modified K-Means. In details, HMLD greatly increased the detection accuracy of DoS attacks and R2L attacks. [ABSTRACT FROM AUTHOR]

The current Internet is an end-to-end communication architecture based on packet switching between terminals. However, with the development of computer networks and increasing proliferation of communication services, the ability of this architecture-based Internet to transmit and process data has encountered challenges, and it has been unable to adapt to the rapid development of today's application requirements. Thus, information-centric networking (ICN) is emerging as a feasible solution and has become a new hot topic for future research in network architecture. The ICN strips content from terminal locations and provides services such as storage and multi-party communication through the publish/ subscribe paradigm. Although the ICN brings new opportunities for network development, there are plenty of problems existing in the ICN that have not been settled. For example, the conflict between polymerizability and mobility in the naming scheme, scalability problems of the name routing, as well as the inefficiency of transmission control algorithms under the circumstance of rich interest are all worth discussing. However, utilizing the computational and analytical features of artificial intelligence (AI) can help the ICN break through the bottleneck of existing technology development. With regard to the issues above, this article introduces and investigates some related technologies of AI in the ICN, that is, naming mechanism, transmission control, and content distribution, and introduces and surveys problems in ICN research. Furthermore, the next development direction of ICN and issues that need to be studied are discussed in this work. [ABSTRACT FROM AUTHOR]

Network virtualization enables the share of a physical network among multiple virtual networks. Virtual network embedding determines the effectiveness of utilization of network resources. Traditional heuristic mapping algorithms follow static procedures, thus cannot be optimized automatically, leading to sub-optimal ranking and embedding decisions. To solve this problem, we introduce a reinforcement learning method to virtual network embedding. In this paper, we design and implement a policy network based on reinforcement learning to make node mapping decisions. We use policy gradient to achieve optimization automatically by training the policy network with the historical data based on virtual network requests. To the best of our knowledge, this work is the first to utilize historical requests data to optimize network embedding automatically. The performance of the proposed embedding algorithm is evaluated in comparison with two other algorithms which use artificial rules based on node ranking. Simulation results show that our reinforcement learning is able to learn from historical requests and outperforms the other two embedding algorithms. [ABSTRACT FROM AUTHOR]

Bones are inflexible yet ever-changing metabolic organs, and bone homeostasis is maintained through two delicately regulated processes: bone construction and bone reabsorption. An imbalance in bone metabolism is linked to most orthopedic diseases, including osteoporosis and rheumatoid arthritis. Importantly, tumor necrosis factor-α (TNF-α) blocks osteoblast differentiation and stimulates osteoclast formation, resulting in delayed deposition of new bone and accelerated bone resorption, especially in rheumatoid arthritis patients with inflammatory conditions. Pilose antler peptide (PAP) isolated and purified from deer antlers has been shown to have beneficial effects on chronic inflammation. In the present study, we studied the impact of PAP on osteoblast differentiation and evaluated the regulatory mechanism, with particular emphasis on the effect of PAP on TNF-α-mediated NF-κB signaling. Mouse primary osteoblast cells were activated with bone morphogenetic protein-2 (BMP-2) for osteoblast differentiation. A significant stimulatory effect of PAP in osteoblastogenesis was observed using ALP activity and Alizarin Red S staining assays. Meanwhile, PAP significantly rescued TNF-α-induced impairment of osteoblast formation as well as mineralization. Furthermore, we found a similar trend upon analyzing osteoblast-specific gene expression. PAP significantly rescued TNF-α-mediated decrease in expression of osteoblast-specific genes. A molecular mechanism assay indicated that PAP significantly inhibited TNF-α-mediated stimulation of NF-κB signaling activity, as well as nuclear translocation of its subunit p65. Moreover, over-expression of p65 reversed the stimulatory effects of PAP on osteoblast differentiation. Furthermore, we also identified that PAP dose dependently inhibit osteoclastogenesis, and this effect might be achieved via suppressing NF-κB activity. In summary, this study shows that PAP promotes osteoblast differentiation and blocks TNF-α-mediated suppression of osteoblastogenesis in vitro via the NF-κB/p65 pathway, as well as inhibits osteoclastsogenesis in vitro. Therefore, PAP, a novel drug with both antiresorptive and osteoanabolic activity, shows therapeutic potential as an alternative treatment for osteolytic diseases, including rheumatoid arthritis and osteoporosis. [ABSTRACT FROM AUTHOR]

Circulating tumor cells (CTCs) quantification may be helpful for evaluating cancer dissemination, predicting prognosis and assessing therapeutic effectiveness and safety. In the present study, CTCs from blood samples of 72 patients with hepatocellular carcinoma (HCC) were enriched with anti-EpCAM nanoparticles. AFP mRNA level was detected by nested polymerase chain reaction (PCR) after enrichment of CTCs from HCC blood samples at 0, 3, 6, 9 and 12 months after hepatectomy, respectively. AFP mRNA expression in CTCs was positive in 43 patients (59.7%) and negative in 29 patients (40.3%) before hepatectomy. Among 43 patients with positive AFP mRNA expression in CTCs before hepatectomy, 10 and 11 were diagnosed as intrahepatic/extrahepatic metastasis before and after hepatectomy, respectively. In addition, these 21 patients with metastasis had persisting positive AFP mRNA of CTCs during the whole tested year. Specifically, 3 patients with AFP mRNA negative in CTCs before hepatectomy changed to be positive at 6 and 9 months, and 2 of them were diagnosed as metastasis 12 months after hepatectomy. We conclude that the positive AFP mRNA of CTCs can be a pivotal predictor for HCC metastasis before and after hepatectomy. The release of AFP expression from hepatocellular carcinoma cells into circulation must be a major source of HCC metastasis. [ABSTRACT FROM AUTHOR]

Picobirnaviruses (PBVs) infect humans and a wide range of animals and may cause diarrhea. The aim of this study was to investigate PBV infection and its association with diarrhea. Here, seven PBV RT-PCR-positive fecal samples from diarrheic children and four fecal samples from healthy children as controls were analyzed by viral metagenomics. The results indicated that all the seven diarrheic fecal samples contain high titers of PBV sequences, while three of the controls were negative, and one had low titers of PBV. Three of the diarrheic fecal samples were also positive for other viruses, including anellovirus, human gyrovirus, human parechovirus, and porcine stool-associated circular virus. PBV sequences from the seven patients were assembled, generating seven large contigs with the complete ORF of RNA-dependent RNA polymerase (RdRp). Phylogenetic analysis of the amino acid sequences of RdRp indicated that the seven PBVs in the present study belonged to three different genogroups. Our data suggest that PBV might have been the cause of diarrhea in these seven children. [ABSTRACT FROM AUTHOR]

Background: Rates of rickets from 15.9 to 26.7% have been reported in China. Methods: Combining the methods of epidemiology and the behavioral sciences, this study investigated the prevalence of rickets in children in rural Shanxi Province, China. A total of 250 children age 12–24 months were examined physically for the presence of rickets, blood was drawn for laboratory analysis, and X-rays were taken of each child’s wrists. Results: Vitamin D deficiency in the spring was found among 65.3% of children. Rickets diagnosis relying on clinical signs alone determined a rickets prevalence of 41.6%, declining to 17.0% in the fall after a summer of sun exposure (χ2 = 8.356, P = 0.004). But an integrated diagnostic method exploiting clinical signs, X-ray and alkaline phosphatase levels found the prevalence of active rickets to be 3.7%. Furthermore, it was demonstrated that only five clinical signs reflect active rickets – wide wrists, frontal bossing, rachitic rosary, Harrison’s sulcus, and bowed legs. Conclusions: The prevalence of active rickets in young children in northern China is lower than previously reported. Even in poor countries, simple tests such as X-rays and alkaline phosphatase can be added to physical examination to more accurately diagnose active rickets. [ABSTRACT FROM AUTHOR]

• Investigating the neural correlates of processing self related positive traits. • Self-positivity bias modulated N400 components. • Provide neuro-basis of self-positivity bias in subliminal self-cue. Previous studies studies indicate that individuals tend to integrate positive information into their self-concept. However, whether such self-positivity bias would still be observed without an explicit self-related cue is unknown. In the present study, 29 participants were asked to evaluate a series of positive and negative trait adjectives, after the participants were presented with their own name or another name subliminally. During the task, their electroencephalograms were recorded. The results showed participants responded faster to positive traits than to negative traits in the self-name cue conditions. In addition, both the latencies and the amplitudes of the N400 showed significant interaction between name-cue and valence in N400 (240−440 ms) amplitudes. The earlier N400 latencies and smaller N400 amplitudes were associated with positive traits in the self-name cue. These results suggested that the self-positivity bias can also be observed in a subliminally presenting self-cue, indicating the robustness of self-positivity bias. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • SUMO1P3 expression is increased in NSCLC tissue and cells. • SUMO1P3 expression is corrected with clinical progression in NSCLC. • SUMO1P3 negatively regulates miR-136 by directly targeting. • SUMO1P3 promotes NSCLC cell migration and invasion through suppressing miR-136. Abstract Long non-coding RNA small ubiquitin-like modifier 1 pseudogene 3 (SUMO1P3) is located on chromosome 1q23.2, and has been suggested to serve as oncogenic lncRNA in many kinds of human malignancy. The role of SUMO1P3 in non-small cell lung cancer (NSCLC) was still unknown. In our study, we analyzed The Cancer Genome Atlas (TCGA) database, and observed SUMO1P3 expression was increased in both lung squamous cell carcinoma and lung adenocarcinoma. Then, we confirmed that SUMO1P3 expression was significantly increased in NSCLC cancer tissues and cell lines. Meanwhile, the expression levels of SUMO1P3 expression in metastatic lymph node specimens were strikingly elevated in comparison to primary NSCLC tissue specimens. Then, we found high SUMO1P3 expression was correlated with late clinical stage, lymph node metastasis, distant metastasis and poor differentiated degree. In the survival analysis of TCGA, we observed that SUMO1P3 expression had no association with overall survival and disease free survival in NSCLC patients. There was a statistically negative correlation between SUMO1P3 expression and miR-136 expression in NSCLC tissues. Moreover, miR-136 directly bound to SUMO1P3, and SUMO1P3 negatively regulated miR-136 expression in NSCLC cells. Furthermore, SUMO1P3 promoted NSCLC cell migration and invasion via regulating miR-136. In conclusion, SUMO1P3 functions as metastasis-associated lncRNA in NSCLC. [ABSTRACT FROM AUTHOR]