A pricing model for subscriptions in data transactions

With the increasing demands for data, the subscription scheme came into being in the face of pricing for an extensive and unfixed number of data items. However, in the existing subscription scheme, a diversity of customers in the real market may lead to the lack of stability, which means risking the failure of pricing. Additionally, the study involves arbitrage-free, an essential economics concept, which is not reasonable on data items. To address these problems, this paper provides insights for designing an improved subscription scheme that includes two components: the calculation and the specific validity. On the one hand, the calculation improves the existing scheme by building a new structure that combines different customers' behaviours instead of the separated calculation in the existing scheme, and can steadily set prices for subscriptions to maximise the sellers' profit even in a real market. On the other hand, the specific validity shows the improvement towards arbitrage-free by taking the characteristics of data subscriptions into account. In other words, the specific validity endows the scheme with more rationality

Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

Abstract The Industrial Internet of Things (IIoTs) is creating a new world which incorporates machine learning, sensor data, and machine-to-machine (M2M) communications. In IIoTs, the length of the transmission delay is one of the pivotal performance because dilatory communication will cause heavy losses to industrial applications. In this paper, a learning-based synchronous (LS) approach from forwarding nodes is proposed to reduce the delay for IIoTs. In an asynchronous Media Access Control protocol, when senders need to send data, they always require to wait for their corresponding receiver to wake up. Thus, the delay here is greater than in the synchronous network. However, the synchronization cost of the whole network is enormous, and it is difficult to maintain. Therefore, LS mechanism uses a partial synchronization approach to reduce synchronization costs while effectively reducing delay. In LS approach, instead of synchronizing the nodes in the entire network, only sender nodes and part of the nodes in their forwarding node set are synchronized by self-learning methods, and accurate synchronization is not required here. Thus, the delay can be effectively reduced under the low cost. Secondly, the nodes near sink maintain the original duty cycle, while the nodes in the regions away from the sink use their remaining energy and perform synchronization operations, so as not to damage the network lifetime. Finally, because the synchronization in this paper is based on different synchronization periods among different nodes, it can improve the network performance by reducing the conflict between simultaneous data transmission. The theoretical analysis results show that compared with the previous approach FFSC, LS approach can reduce the end-to-end delay by 5.13–11.64% and increase the energy efficiency by 14.29–17.53% under the same lifetime with a more balanced energy utilization

Effect of Autophagy Regulated by Sirt1/FoxO1 Pathway on the Release of Factors Promoting Thrombosis from Vascular Endothelial Cells

Factors promoting thrombosis such as von Willebrand factor (vWF) and P-selectin are essential for the development of atherosclerosis (AS) and arterial thrombosis. The processing, maturation and release of vWF are regulated by autophagy of vascular endothelial cells. The Sirt1/FoxO1 pathway is an important pathway to regulate autophagy of endothelial cells, therefore the Sirt1/FoxO1 pathway may be an important target for the prevention of thrombosis. We investigated the role of ox-LDL in the release of vWF and P-selectin and the expression of Sirt1 and FoxO1 by Western Blot, Flow Cytometry, ELISA, and tandem fluorescent mRFP-GFP-LC3. We found that vWF and P-selectin secretion increased and Sirt1/FoxO1 pathway was depressed in human umbilical vein endothelial cells (HUVEC) when treated with ox-LDL. Moreover, the expression of autophagy-related protein LC3-II/I and p62 increased. Then, we explored the relationship between autophagy regulated by the Sirt1/FoxO1 pathway and the secretion of vWF and P-selectin. We found that Sirt1/FoxO1, activated by the Sirt1 activators resveratrol (RSV) and SRT1720, decreased the secretion of vWF and P-selectin, which can be abolished by the autophagy inhibitor 3-MA. The expression of Rab7 increased when Sirt1/FoxO1 pathway was activated, and the accumulation of p62 was decreased. Autophagy flux was inhibited by ox-LDL and Sirt1/FoxO1 pathway might enhance autophagy flux through the promotion of the Rab7 expression. Taken together, our data suggest that by enhancing autophagy flux and decreasing the release of vWF and P-selectin, the Sirt1/FoxO1 pathway may be a promising target to prevent AS and arterial thrombosis

A Wavelet-Driven Subspace Basis Learning Network for High-Resolution Synthetic Aperture Radar Image Classification

The feature learning strategy of convolutional neural networks learns the deep spatial features from high-resolution (HR) synthetic aperture radar (SAR) images while ignoring the speckle noise based on the SAR imaging mechanism. In the feature learning module, the noise reduction by feature-adaptive projection guided by a powerful embedded wavelet feature reconstruction mechanism can effectively learn the deep feature statistics. In this article, we present a wavelet-driven subspace basis learning network (WDSBLN), following an encoder–decoder architecture, for the HR SAR image classification. The powerful wavelet module, including wavelet decomposition and reconstruction, is employed for keeping the structures of learned features well under speckle noise. Specifically, a compact second-order feature enhancement mechanism is designed for improving the contour and edge information of low-frequency components in the feature decomposition stage, and a local feature attention module based on the point-wise convolutional layer is adopted to aggregate the contextual information of the local channel and reserves detail information in the high-frequency components. Then, the reconstructed feature map is employed as a guided standard in the subspace basis learning (SBL) module. The SBL module, including basis generation (generating the subspace basis vectors) and subspace projection (transforming deep feature maps into a signal subspace), maintains the local structure of HR SAR image patches and acquires the robust feature statistics. We conduct evaluations on three real HR SAR image classification datasets, achieving superior performances as compared to other related networks

An Effective Delay Reduction Approach through a Portion of Nodes with a Larger Duty Cycle for Industrial WSNs

For Industrial Wireless Sensor Networks (IWSNs), sending data with timely style to the stink (or control center, CC) that is monitored by sensor nodes is a challenging issue. However, in order to save energy, wireless sensor networks based on a duty cycle are widely used in the industrial field, which can bring great delay to data transmission. We observe that if the duty cycle of a small number of nodes in the network is set to 1, the sleep delay caused by the duty cycle can be effectively reduced. Thus, in this paper, a novel Portion of Nodes with Larger Duty Cycle (PNLDC) scheme is proposed to reduce delay and optimize energy efficiency for IWSNs. In the PNLDC scheme, a portion of nodes are selected to set their duty cycle to 1, and the proportion of nodes with the duty cycle of 1 is determined according to the energy abundance of the area in which the node is located. The more the residual energy in the region, the greater the proportion of the selected nodes. Because there are a certain proportion of nodes with the duty cycle of 1 in the network, the PNLDC scheme can effectively reduce delay in IWSNs. The performance analysis and experimental results show that the proposed scheme significantly reduces the delay for forwarding data by 8.9~26.4% and delay for detection by 2.1~24.6% without reducing the network lifetime when compared with the fixed duty cycle method. Meanwhile, compared with the dynamic duty cycle strategy, the proposed scheme has certain advantages in terms of energy utilization and delay reduction

A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid-Coded HLO

In recent years, the combustion furnace has been widely applied in many different fields of industrial technology, and the accurate detection of combustion states can effectively help operators adjust combustion strategies to improve combustion utilization and ensure safe operation. However, the combustion states inside the industrial furnace change according to the production needs, which further challenges the optimal set of model parameters. To effectively segment the flame pixels, a novel segmentation method for furnace flame using adaptive color model and hybrid-coded human learning optimization (AHcHLO) is proposed. A new adaptive color model with mixed variables (NACMM) is designed for adapting to different combustion states, and the AHcHLO is developed to search for the optimal parameters of NACMM. Then, the best NACMM with optimal parameters is adopted to segment the combustion flame image more precisely and effectively. Finally, the experiment results show that the developed AHcHLO obtains the best-known overall results so far on benchmark functions and the proposed NACMM outperforms state-of-the-art flame segmentation approaches, providing a high detection accuracy and a low false detection rate

Further results on delay-dependent stability criteria of discrete systems with an interval time-varying delay

This paper deals with stability of discrete-time systems with an interval-like time-varying delay. By constructing a novel augmented Lyapunov functional and using an improved finite-sum inequality to deal with some sum-terms appearing in the forward difference of the Lyapnov functional, a less conservative stability criterion is obtained for the system under study if compared with some existing methods. Moreover, as a special case, the stability of discrete-time systems with a constant time delay is also investigated. Three numerical examples show that the derived stability criteria are less conservative and require relatively small number of decision variables