Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to ...collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.
An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud ( DGC ) systems for low response time and high ...cost-effectiveness in recent years. Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption. Many factors in DGCs, e.g., prices of power grid, and the amount of green energy express strong spatial variations. The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations. This work adopts a G / G / 1 queuing system to analyze the performance of servers in DGCs. Based on it, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm ( SBA ) to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs, and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications. Realistic data-based experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.
Our study aims to verify the potential effects and underlying mechanisms of IL-37 in Coxsackievirus B3 (CVB3)-induced viral myocarditis (VMC). VMC model was established by intraperitoneal injection ...of CVB3 into 6-week-old male balb-c mice on day 0. Each mouse of the IL-37-control group and IL-37-VMC CVB3 groups was intraperitoneally injected with IL-37 on day 4 and day 7. The cardiac function was evaluated by transthoracic echocardiography including LVEF, LVFE, IVSs and IVSd. Myocardial injury was measured by Elisa for serum cTnI. The inflammation infiltration and fibrosis were evaluated by hematoxylin and eosin (HE) staining and Masson staining. The expression levels of NLRP3 inflammasome components in pyroptosis were determined by western blot, Elisa, and immunofluorescent analysis. We also detected the expression of IL-37-IL-1R8 in PBMCs by immunofluorescence after injection with CVB3 and IL-37. Compared with the VMC group, mice received CVB3 and IL-37 have improved cardiac function, reduced inflammation infiltration and fibrosis, and with lower expression of cTnI, IL-1β, IL-18 and NLRP3 inflammasome component. IL-37 weakened the upregulation of GSDMD and phosphorylation of NF-κB p65 induced by CVB3. Exogenous addition of IL-37 with CVB3 further increases the production of IL-37-IL-1R8 -IL-18RA complex in vitro. Our findings indicate that IL-37 alleviates CVB3-induced VMC, which may be produced by inhibiting NLRP3 inflammasome-mediated pyroptosis, NF-κB signaling pathway, and IL-37-IL-1R8 -IL-18RA complex.
Many applications, e.g., Uber, collect large-scale trajectory data from moving vehicles on road network. Trajectory data analytics can benefit many real-world applications, such as route planning and ...transportation optimizations. Two core operations in trajectory data analytics are trajectory similarity search and join, and both of them rely on a trajectory similarity function to measure the similarity between two trajectories. However, existing similarity functions focus on trajectory points distance and neglect the fact the trajectories should be on road network. Obviously aligning trajectories on road network can remove the noise points introduced by system errors. Toward this goal, we define a road-network-aware trajectory similarity function to measure trajectory similarity. To support trajectory similarity search and join, we propose a filtering-refine framework. In the filtering step, we compute a signature of each trajectory such that if two trajectories are similar, they must share a common signature. We utilize the signatures to prune a huge number of dissimilar pairs. In the refine step, we design effective algorithms to verify the candidates that are not pruned in the filtering step. To support large-scale trajectories, we develop a system DISON for Distributed In-Memory Trajectory Similarity Search and Join on Road Network. DISON splits trajectories into disjoint partitions by considering load balance and locality, and designs effective global index to prune irrelevant partitions. Extensive experiments on real datasets showed that our method achieved high effectiveness, efficiency, and scalability and outperformed existing solutions significantly.
A COVID-19 outbreak started in Wuhan, China, last December and now has become a global pandemic. The clinical information in caring of critically ill patients with COVID-19 needs to be shared timely, ...especially under the situations that there is still a largely ongoing spread of COVID-19 in many countries.
A multicenter prospective observational study investigated all the COVID-19 patients received in 19 ICUs of 16 hospitals in Wuhan, China, over 24 h between 8 AM February 2h and 8 AM February 27, 2020. The demographic information, clinical characteristics, vital signs, complications, laboratory values, and clinical managements of the patients were studied.
A total of 226 patients were included. Their median (interquartile range, IQR) age was 64 (57-70) years, and 139 (61.5%) patients were male. The duration from the date of ICU admission to the study date was 11 (5-17) days, and the duration from onset of symptoms to the study date was 31 (24-36) days. Among all the patients, 155 (68.6%) had at least one coexisting disease, and their sequential organ failure assessment score was 4 (2-8). Organ function damages were found in most of the patients: ARDS in 161 (71.2%) patients, septic shock in 34 (15.0%) patients, acute kidney injury occurred in 57 (25.2%) patients, cardiac injury in 61 (27.0%) patients, and lymphocytopenia in 160 (70.8%) patients. Of all the studied patients, 85 (37.6%) received invasive mechanical ventilation, including 14 (6.2%) treated with extracorporeal membrane oxygenation (ECMO) at the same time, 20 (8.8%) received noninvasive mechanical ventilation, and 24 (10.6%) received continuous renal replacement therapy. By April 9, 2020, 87 (38.5%) patients were deceased and 15 (6.7%) were still in the hospital.
Critically ill patients with COVID-19 are associated with a higher risk of severe complications and need to receive an intensive level of treatments. COVID-19 poses a great strain on critical care resources in hospitals.
Chinese Clinical Trial Registry, ChiCTR2000030164. Registered on February 24, 2020, http://www.chictr.org.cn/edit.aspx?pid=49983&htm=4.
The economy of scale provided by cloud attracts a growing number of organizations and industrial companies to deploy their applications in cloud data centers (CDCs) and to provide services to users ...around the world. The uncertainty of arriving tasks makes it a big challenge for private CDC to cost-effectively schedule delay bounded tasks without exceeding their delay bounds. Unlike previous studies, this paper takes into account the cost minimization problem for private CDC in hybrid clouds, where the energy price of private CDC and execution price of public clouds both show the temporal diversity. Then, this paper proposes a temporal task scheduling algorithm (TTSA) to effectively dispatch all arriving tasks to private CDC and public clouds. In each iteration of TTSA, the cost minimization problem is modeled as a mixed integer linear program and solved by a hybrid simulated-annealing particle-swarm-optimization. The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.
Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet, resulting in the formation of defects. The present study develops a novel method for automatically detect ...typical undercuts, pores and burn-through defects from CCD using concepts of Convolutional Neural Network(CNN) and active vision. Four deep learning methods of training a defects classifier using 1) an AlexNet built from scratch, 2) a Visual Geometry Group 16 (VGG16) built from scratch, 3) the output features of the VGG16 network architecture previously trained on the general ImageNet dataset, 4) the convolutional block attention module adaptively refining the intermediate feature map of VGG16 are investigated. To detect the weld defects, laser streak images of different weld defects are collected and two types of data augmentation are used to reduce overfitting caused by the limited training dataset. In the method 1 and 2, the improvement of accuracy by data augmentation is gradually slow. VGG16 model in combination with data augmentation 2 achieve the state-of-the-art performance for defects detection in the galvanized steel sheets lap joint. Transfer learning methods using the output features of VGG16 and attention mechanism methods introducing the convolutional block attention module achieve improved accuracy in the case of a small number of training dataset. And all classifiers have a 100 % recognition rate for good welds. This shows that the method of combining CNN and laser vision is feasible, and this method can be used to record the number and location of typical defects in continuous welds.
Rational integration of native enzymes and nanoscaffold is an efficient means to access robust biocatalyst, yet remains on-going challenges due to the trade-off between fragile enzymes and harsh ...assembling conditions. Here, we report a supramolecular strategy enabling the in situ fusion of fragile enzymes into a robust porous crystal. A c2-symmetric pyrene tecton with four formic acid arms is utilized as the building block to engineer this hybrid biocatalyst. The decorated formic acid arms afford the pyrene tectons high dispersibility in minute amount of organic solvent, and permit the hydrogen-bonded linkage of discrete pyrene tectons to an extended supramolecular network around an enzyme in almost organic solvent-free aqueous solution. This hybrid biocatalyst is covered by long-range ordered pore channels, which can serve as the gating to sieve the catalytic substrate and thus enhance the biocatalytic selectivity. Given the structural integration, a supramolecular biocatalyst-based electrochemical immunosensor is developed, enabling the pg/mL detection of cancer biomarker.
As cloud computing is becoming growingly popular, consumers' tasks around the world arrive in cloud data centers. A private cloud provider aims to achieve profit maximization by intelligently ...scheduling tasks while guaranteeing the service delay bound of delay-tolerant tasks. However, the aperiodicity of arrival tasks brings a challenging problem of how to dynamically schedule all arrival tasks given the fact that the capacity of a private cloud provider is limited. Previous works usually provide an admission control to intelligently refuse some of arrival tasks. Nevertheless, this will decrease the throughput of a private cloud, and cause revenue loss. This paper studies the problem of how to maximize the profit of a private cloud in hybrid clouds while guaranteeing the service delay bound of delay-tolerant tasks. We propose a profit maximization algorithm (PMA) to discover the temporal variation of prices in hybrid clouds. The temporal task scheduling provided by PMA can dynamically schedule all arrival tasks to execute in private and public clouds. The sub problem in each iteration of PMA is solved by the proposed hybrid heuristic optimization algorithm, simulated annealing particle swarm optimization (SAPSO). Besides, SAPSO is compared with existing baseline algorithms. Extensive simulation experiments demonstrate that the proposed method can greatly increase the throughput and the profit of a private cloud while guaranteeing the service delay bound.
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