A Community-Based Event Delivery Protocol in Publish/Subscribe Systems for Delay Tolerant Sensor Networks

The basic operation of a Delay Tolerant Sensor Network (DTSN) is to finish pervasive data gathering in networks with intermittent connectivity, while the publish/subscribe (Pub/Sub for short) paradigm is used to deliver events from a source to interested clients in an asynchronous way. Recently, extension of Pub/Sub systems in DTSNs has become a promising research topic. However, due to the unique frequent partitioning characteristic of DTSNs, extension of a Pub/Sub system in a DTSN is a considerably difficult and challenging problem, and there are no good solutions to this problem in published works. To ad apt Pub/Sub systems to DTSNs, we propose CED, a community-based event delivery protocol. In our design, event delivery is based on several unchanged communities, which are formed by sensor nodes in the network according to their connectivity. CED consists of two components: event delivery and queue management. In event delivery, events in a community are delivered to mobile subscribers once a subscriber comes into the community, for improving the data delivery ratio. The queue management employs both the event successful delivery time and the event survival time to decide whether an event should be delivered or dropped for minimizing the transmission overhead. The effectiveness of CED is demonstrated through comprehensive simulation studies

Filter Pruning For CNN With Enhanced Linear Representation Redundancy

Structured network pruning excels non-structured methods because they can take advantage of the thriving developed parallel computing techniques. In this paper, we propose a new structured pruning method. Firstly, to create more structured redundancy, we present a data-driven loss function term calculated from the correlation coefficient matrix of different feature maps in the same layer, named CCM-loss. This loss term can encourage the neural network to learn stronger linear representation relations between feature maps during the training from the scratch so that more homogenous parts can be removed later in pruning. CCM-loss provides us with another universal transcendental mathematical tool besides L*-norm regularization, which concentrates on generating zeros, to generate more redundancy but for the different genres. Furthermore, we design a matching channel selection strategy based on principal components analysis to exploit the maximum potential ability of CCM-loss. In our new strategy, we mainly focus on the consistency and integrality of the information flow in the network. Instead of empirically hard-code the retain ratio for each layer, our channel selection strategy can dynamically adjust each layer's retain ratio according to the specific circumstance of a per-trained model to push the prune ratio to the limit. Notably, on the Cifar-10 dataset, our method brings 93.64% accuracy for pruned VGG-16 with only 1.40M parameters and 49.60M FLOPs, the pruned ratios for parameters and FLOPs are 90.6% and 84.2%, respectively. For ResNet-50 trained on the ImageNet dataset, our approach achieves 42.8% and 47.3% storage and computation reductions, respectively, with an accuracy of 76.23%. Our code is available at https://github.com/Bojue-Wang/CCM-LRR

Combined evaluation of arterial stiffness and blood pressure promotes risk stratification of peripheral arterial disease

Background: Previous studies have reported the separate association of arterial stiffness (AS) and blood pressure with peripheral arterial disease (PAD). Objectives: The aim of this study was to investigate the risk stratification capacity of AS on incident PAD beyond blood pressure status. Methods: A total of 8,960 participants from Beijing Health Management Cohort were enrolled at the first health visit between 2008 and 2018 and then followed until the incidence of PAD or 2019. Elevated AS was defined as brachial-ankle pulse-wave velocity (baPWV) \u3e 1,400 cm/s, including moderate stiffness (1,400 ≤ baPWV \u3c 1,800 cm/s) and severe stiffness (baPWV ≥ 1,800 cm/s). PAD was defined as ankle-brachial index \u3c 0.9. A frailty Cox model was used to calculate the HR, integrated discrimination improvement, and net reclassification improvement. Results: During follow-up, 225 participants (2.5%) developed PAD. After adjusting for confounding factors, the highest risk for PAD was observed in the group with elevated AS and blood pressure (HR: 2.253; 95% CI: 1.472-3.448). Among participants with ideal blood pressure and those with well-controlled hypertension, PAD risk was still significant for severe AS. The results remained consistent in multiple sensitivity analyses. In addition, baPWV significantly improved the predictive capacity for PAD risk beyond systolic and diastolic blood pressures (integrated discrimination improvement 0.020 and 0.190, net reclassification improvement 0.037 and 0.303). Conclusions: This study suggests the clinical importance of combined evaluation and control of AS and blood pressure for the risk stratification and prevention of PAD

Changes in glycosylated proteins in colostrum and mature milk and their implication

IntroductionGlycosylation is one of the essential post-translational modifications that influences the function of milk proteins.MethodsIn the present study, 998 proteins and 764 glycosylated sites from 402 glycoproteins were identified in human milk by TMT labeling proteomics. Compared to human milk proteins, the glycoproteins were mainly enriched in cell adhesion, proteolysis, and defense/immune process.ResultsThe abundance of 353 glycosylated sites and their 179 parent proteins was quantified. After normalization to their parent protein’s abundance, 78 glycosylated sites in 56 glycoproteins and 10 glycosylated sites in 10 glycoproteins were significantly higher in colostrum and mature milk, respectively. These changed glycoproteins were mainly related to host defense. Intriguingly, one glycosylated site (Asp144) in IgA and two glycosylated sites (Asp38 and Asp1079) in tenascin are significantly upregulated even though their protein abundance was downregulated during lactation.DiscussionThis study helps us figure out the critical glycosylated sites in proteins that might influence their biological function in an unbiased way

Enhancement of stress tolerance in transgenic tobacco plants constitutively expressing AtIpk2β, an inositol polyphosphate 6-/3-kinase from Arabidopsis thaliana

Inositol phosphates (IPs) and their turnover products have been implicated to play important roles in stress signaling in eukaryotic cells. In higher plants genes encoding inositol polyphosphate kinases have been identified previously, but their physiological functions have not been fully resolved. Here we expressed Arabidopsis inositol polyphosphate 6-/3-kinase (AtIpk2β) in two heterologous systems, i.e. the yeast Saccharomycescerevisiae and in tobacco (Nicotiana tabacum), and tested the effect on abiotic stress tolerance. Expression of AtIpk2β rescued the salt-, osmotic- and temperature-sensitive growth defects of a yeast mutant strain (arg82Δ) that lacks inositol polyphosphate multikinase activity encoded by the ARG82/IPK2 gene. Transgenic tobacco plants constitutively expressing AtIpk2β under the control of the Cauliflower Mosaic Virus 35S promoter were generated and found to exhibit improved tolerance to diverse abiotic stresses when compared to wild type plants. Expression patterns of various stress responsive genes were enhanced, and the activities of anti-oxidative enzymes were elevated in transgenic plants, suggesting a possible involvement of AtIpk2β in plant stress responses

How does root biodegradation after plant felling change root reinforcement to soil?

International audienceAims : Previous studies have shown that root reinforcement provided by trees could decrease over time after stem cutting. The short-term changes in root mechanical and structural traits associated with root reinforcement after stem cutting have not been fully studied. We aimed to quantify the temporal changes in root mechanical and structural traits following stem cutting, and to identify the major drivers of root reinforcement deterioration.Methods : At six elapsed times (zero, one, three, six, nine, and twelve months) after stem cutting of the species Symplocos setchuensis Brand, we measured shear strength for both rooted and root free soils, root failure modes, root mechanical traits (tensile strength, Young’s modulus, and tensile strain) and structural traits (cellulose, hemicellulose and lignin contents).Results : Both root mechanical and structural traits significantly differed as a function of root diameter and time after stem cutting. Tensile strength decreased 19.7% while Young’s modulus decreased 46.9% twelve months after stem cutting. Hemicellulose content showed the greatest decrease (45.3%) among the structural traits. The relative reduction in magnitude was higher for fine roots than coarse roots. Additional shear strength (at the yield point) provide by roots decreased 85.9% twelve months after stem cutting.Conclusions : Our findings demonstrate a higher rate of root reinforcement deterioration after stem cutting than previously reported in literatures. Our results suggest that the underlying mechanism of deterioration of root reinforcement is more likely caused by a shift of root failure from tensile breakage to slide-out failure, and a decline in root Young’s modulus

Parking backbone : toward efficient overlay routing in VANETs

In vehicular ad hoc networks (VANETs), message dissemination to one or more vehicles is very challenging, due to frequent network disconnections and uncertain locations of the destination vehicles. Deploying roadside units (RSUs) is a possible solution to overcome the challenges, but it often requires a large amount of investment. In this paper, we propose the idea of Parking Backbone, which does not need any RSUs but leverages a virtual overlay network formed by outside parked vehicles to track vehicles and to disseminate messages between moving vehicles. Our scheme consists of three parts. At first, to each road, parked vehicles both at roadside and off street are grouped into a cluster as far as possible. An urban overlay network is established based on this type of clusters for data transmission. Secondly, to a specific vehicle, a daily mobility model is established, to determine its location through a corresponding location prediction algorithm. Finally, a novel message delivery scheme is designed to efficiently transmit messages to destination vehicles through the proposed virtual overlay network. Thanks to the extensive and stable outside parking in cities, once grouped into the overlay structure, data transmission can be easily achieved over the Parking Backbone. Extensive simulation results prove that our scheme achieves high performance in message dissemination.Published versio

Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles

Rogue nodes in the Internet of vehicles (IoV) bring traffic congestion, vehicle collision accidents and other problems, which will cause great social losses. Therefore, rogue node discovery plays an important role in building secure IoV environments. Existing machine learning-based rogue node detection methods rely too much on historical data, and these methods may lead to long network delay and slow detection speed. Moreover, methods based on Roadside Units (RSUs) have poor performance if the number of RSUs is insufficient. Based on the widespread presence of ground vehicles, we propose a rogue node detection scheme based on the fog network formed by roadside parked vehicles. To achieve efficient rogue node discovery, a fog network composed of stable roadside parked vehicles is dynamically established, in which each fog node firstly collects the information of moving vehicles on the road in its coverage range, and then fog nodes use the U-test method to determine the rogue nodes in parallel, so as to find the bad nodes efficiently. Simulation results show that the proposed algorithm has higher detection accuracy and stability than the other rogue node detection schemes

Unmanned Aerial Vehicle Computation Task Scheduling Based on Parking Resources in Post-Disaster Rescue

Natural disasters bring huge loss of life and property to human beings. Unmanned aerial vehicles (UAVs) own the advantages of high mobility, high flexibility, and rapid deployment, and are important equipment during post-disaster rescue. However, UAVs usually have restricted battery and computing power. They are not fit for performing compute-intensive tasks during rescue. Since there are widespread parking resources in a city, multiple parked vehicles working together to compute the applications from UAVs in a post-disaster rescue is investigated to ensure the quality of experience (QoE) of the UAVs. To execute uploaded task effectively, surviving parked vehicles within the monitoring range of an UAV are arranged into a cluster as much as possible. Then, the task execution cost is analyzed. Furthermore, a deep reinforcement learning (DRL)-based offloading policy is constructed, which interacts with the environment in an intelligent way to achieve optimization goals. The simulation experiments show that the proposed offloading scheme has a higher task completion rate and a lower task execution cost than other baselines schemes

Underdetermined DOA Estimation of Wideband LFM Signals Based on Gridless Sparse Reconstruction in the FRF Domain

An underdetermined direction of arrival (DOA) estimation method of wideband linear frequency modulated (LFM) signals is proposed without grid mismatch. According to the concentration property of LFM signal in the fractional Fourier (FRF) domain, the received sparse model of wideband signals with time-variant steering vector is firstly derived based on a coprime array. Afterwards, by interpolating virtual sensors, a virtual extended uniform linear array (ULA) is constructed with more degrees of freedom, and its covariance matrix in the FRF domain is recovered by employing sparse matrix reconstruction. Meanwhile, in order to avoid the grid mismatch problem, the modified atomic norm minimization is used to retrieve the covariance matrix with the consecutive basis. Different from the existing methods that approximately assume the frequency and the steering vector of the wideband signals are time-invariant in every narrowband frequency bin, the proposed method not only can directly solve more DOAs of LFM signals than the number of physical sensors with time-variant frequency and steering vector, but also obtain higher resolution and more accurate DOA estimation performance by the gridless sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed method