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With the advance of smart manufacturing and information technologies, the volume of data to process is increasing accordingly. Current solutions for big data processing resort to distributed stream processing systems, such as Apache Flink and Spark. However, such frameworks face challenges of resource underutilization and high latency in big data application scenarios. In this article, we propose SPSC, a serverless-based stream computing framework where events are discretized into the atomic stream and stateless Lambda functions are taken as context-irrelevant operators, achieving task parallelism and inherent data parallelism in processing. Also, we implement a prototype of the framework on Amazon Web service (AWS) using AWS Lambda, AWS simple queue service, and AWS DynamoDB. The evaluation shows that compared with Alibaba’s real-time computing Flink version, SPSC outperforms by 10.12% when the overhead is close.

Although poly(vinyl alcohol) (PVA) hydrogel has high elasticity and is suitable for cartilage tissue engineering, it is difficult to have both high strength and toughness. In this study, a simple and universal strategy is proposed to prepare strong and tough PVA hydrogels by in situ forming nanofibers on the original network structure induced by a molecular chain rearrangement. Quenching–tempering alteratively in ethanol and water several times is carried out to strengthen PVA hydrogels (PVA-Etnhydrogels) due to the advantages of noncovalent bonds in adjustability and reversibility. The results show that, after three quenching–tempering cycles, PVA-Et3hydrogel with water content up to 79 wt % shows comprehensive improved mechanical properties. The compression modulus, tensile modulus, fracture strength, tensile strain, and tear energy of the PVA-Et3hydrogel are 270, 250, 260, 130, and 180% of the initial PVA hydrogel, respectively. The improved mechanical properties of the PVA-Et3hydrogel are attributed to the strong cross-linked PVA chains and hydrogen bond-reinforced nanofibers. This study not only provides a simple and efficient solution for the preparation of strong and tough polymer scaffolds in tissue engineering but also provides new insights for understanding the mechanism of improving the mechanical properties of polymer hydrogels by adjusting the molecular structure.

The optimal control problem of cyber-physical systems (CPSs) under network attacks is of practical significance, but it has yet to be thoroughly studied. This article focuses on this problem, and proposes an adaptive fuzzy inverse optimal output feedback control strategy for a class of nonlinear CPSs suffering from actuator and sensor attacks. First, a state transformation is implemented to change the attacked system into a new system for which output feedback control becomes feasible. Moreover, the Nussbaum gain function method is adopted to overcome the design difficulties caused by uncertain network attacks and unknown control coefficients. By combining inverse optimal control design theory, fuzzy logic systems method, and adaptive backstepping technique, a novel adaptive inverse optimal output feedback control scheme is developed to guarantee the security of the closed-loop system, i.e., the semi-global uniform ultimate boundedness of all signals in the closed-loop system. Meanwhile, the cost function is minimized to meet the control requirement on low power consumption. At last, the effectiveness and practicality of the proposed control method are verified through two simulation examples.

With the rapid development of information technology, the concept of the Metaverse has swept the world and set off a new wave of the industrial revolution. The construction of living and manufacturing scenes based on the Metaverse requires the joint participation of scientists and engineers from various fields where “human” is at the core. In the Metaverse, predicting human behavior and response based on the deep learning model is meaningful because the prediction results can provide more satisfactory services for participants. Therefore, how to deploy a multi-stage machine learning reasoning model has become the bottleneck to improving the development level of Metaverse. Thanks to its scalability and pay-as-you-go billing model, the emerging serverless computing can effectively cope with the workload of machine learning inference. However, the statelessness of serverless computing and the lack of good GPU resource-sharing support make it difficult to deploy the machine learning model directly on the serverless computing platform to play its advantages. Therefore, we propose SMSS, a stateful model inference service, which is deployed on a serverless computing platform that supports GPU sharing. Since the serverless computing platform does not support stateful workflow execution, SMSS adopts log-based workflow runtime support. We also design a mechanism of two-layer GPU sharing to fully explore the potential of inter-model and intra-model GPU sharing. We evaluate the effectiveness of SMSS with real workloads. Our experimental results show that log-based stateful workflow operation support can ensure the stateful execution of tasks with low overhead but facilitate error location and recovery. Two-layer GPU Sharing can reduce the cold start time of inference tasks to two orders of magnitude at most.

Modern transportation big data features high Volume, Velocity, and Variety, making it more and more challenging to develop an intelligent transportation system for data analysis. Current transportation systems resort to cloud computing to deploy their applications but face two bottlenecks, lack of real-time processing and under-utilization of intelligent roadside devices. We observe that dispersed computing—an emerging paradigm of cloud computing—well fits the requirements of modern transportation systems. It provides real-time response by alleviating data transmission between data sources and cloud servers and fully utilizes smart devices by exploring their computing capacity. Therefore, we design RIDIC, an intelligent transportation system with dispersed computing to provide a real-time response when processing transportation big data. RIDIC abstracts all the heterogeneous smart roadside devices as actors, and its workflow consists of three stages, Actor Registration, Resource Application and Task Execution. We conduct experiments on two real-life traffic scenarios—road vehicle detection and traffic signal recognition—and the results show that RIDIC can utilize edge devices to process transportation big data faster while reducing the demand for device computing resources.

This paper aims to propose a novel distributed model predictive control (MPC) scheme for real-time train regulation in urban metro transportation. Particularly, a nonlinear real-time train regulation model is put forward to minimize the timetable deviations and the control strategies for each trainunder the uncertain disturbances, which is then reformulated into a linear optimization model for easy to solve. By regarding each train as a subsystem, we design the distributed MPC algorithm based on the Dantzig-Wolfe decomposition for the train regulation problem, which decomposes the original optimization problem into numerous smaller and less complicated optimization control problems that can be solved independently. Under the distributed mechanism, we regard each train as a local subsystem, which only interacts with the coordinator, ensuring the flexibility and modularity of the control structure. Numerical cases are provided to demonstrate the effectiveness and robustness of the proposed distributed MPC method.

In this article, some convergence conditions are investigated for the multiple tuning parameters iterative algorithm (MIA) and the single tuning parameter iterative algorithm (SIA), which are proposed to solve the discrete periodic Lyapunov matrix equations related to discrete-time linear periodic systems. First, when all the tuning parameters are selected in the interval (0, 1] and the initial conditions are arbitrarily given, it is proven that the MIA is convergent if and only if the discrete-time linear periodic system is asymptotically stable. In particular, when the coefficient matrices of the considered matrix equations are nonnegative, it is shown that the convergence rate of the MIA increases with the tuning parameter increases from 0 to 1. Moreover, the above convergence results derived for the MIA are extended to the SIA. Furthermore, the searching interval of the optimal tuning parameter for the SIA to achieve the fastest convergence rate is narrowed. Finally, two numerical examples are provided to demonstrate the correctiveness of the proposed theoretical results.

This article presents two new state-of-the-art spatial rain field interpolation convolutional neural networks (SRFICNNs), referred to as learned deviation (LD) and learned interpolation (LI) models, for predicting the point rain rate at finer spatial scales. The main contribution is the successful introduction of the prior-art deep learning technique into high-resolution (HR) rainfall rate prediction with significant improvement in accuracy. This is very important for the effective implementation of fade mitigation techniques for both terrestrial and satellite networks. The comparison of the models’ performances with ground truth (radar measurements) shows that the proposed models give an excellent mean square error (MSE) and structural SIMilarity (SSIM) in rainfall field reconstruction if the network depth falls in the range of 15–25 weight layers. The final model uses 20 layers for HR point rain rate prediction. Further study shows that the LD model offers a faster convergence and yields a more accurate rain rate prediction. In particular, this article compares the rain rate exceedance distribution and Log-Normality property from the model estimates with values calculated from measured data. Results show that the LD model gives a highly accurate estimate of these two indices with the corresponding root mean square (rms) error of $5.1709\times 10^{-4}$ and 0.0013, respectively.

ObjectiveIntrahepatic cholangiocarcinoma (ICC) exhibits very low response rate to immune checkpoint inhibitors (ICIs) and the underlying mechanism is largely unknown. We investigate the tumour immune microenvironment (TIME) of ICCs and the underlying regulatory mechanisms with the aim of developing new target to inhibit tumour growth and improve anti-programmed cell death protein-1 (PD-1) efficacy.DesignTumour tissues from patients with ICC together with hydrodynamic ICC mouse models were employed to identify the key cell population in TIME of ICCs. Functional analysis and mechanism studies were performed using cell culture, conditional knockout mouse model and hydrodynamic transfection ICC model. The efficacy of single or combined therapy with anti-PD-1 antibody, gene knockout and chemical inhibitor were evaluated in vivo.ResultsPolymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) are enriched in advanced ICCs and significantly correlated with N7-methylguanosine tRNA methyltransferase METTL1. Using diverse in vivo cancer models, we demonstrate the crucial immunomodulator function of METTL1 in regulation of PMN-MDSC accumulation in TIME and ICC progression. Mechanistically, CXCL8in human and Cxcl5in mouse are key translational targets of METTL1 that facilitate its function in promoting PMN-MDSC accumulation in TIME and ICC progression in vivo. Co-blockade of METTL1 and its downstream chemokine pathway enhances the anti-PD-1 efficacy in ICC preclinical mouse models.ConclusionsOur data uncover novel mechanisms underlying chemokine regulation and TIME shaping at the layer of messenger RNA translation level and provide new insights for development of efficient cancer immunotherapeutic strategies.

Cracks are commonly occurring defects in metal. Crack monitoring in large-area infrastructures requires numerous sensors and thus increases the weight, complexity, and integrity problem for wire deployment. The electromagnetic (EM) resonator-based sensor enjoys good sensitivity but suffers limited coverage. The perturbation theory governing crack detection for EM resonator-based sensors indicates that the uneven field distribution inside the resonator is the bottleneck behind the sensitivity and coverage. In this article, the EM energy is confined underneath the resonator but evenly distributed along the transmission line through periodically loading interleaved step impedance resonators (SIRs) on the two sides of a microstrip line. In this way, the sensitivity can be maintained and the coverage can be extended. An averaged attenuation constant is studied and extracted as a feature for crack characterization. The measurement normalized sensitivity is −0.057 N.U./mm2. This article provides a paradigm to bridge the gap between local and global monitoring. The proposed sensor can be used for monitoring potential cracks of in-service complex components.

This paper focuses on constructing a conjugate gradient-based (CGB) method to solve the generalized periodic coupled Sylvester matrix equations in complex space. The presented method is developed from a point of conjugate gradient methods. It is proved that the presented method can find the solution of the considered matrix equations within finite iteration steps in the absence of round-off errors by theoretical derivation. Some numerical examples are provided to verify the convergence performance of the presented method, which is superior to some existing numerical algorithms both in iteration steps and computation time. [ABSTRACT FROM AUTHOR]

In this paper, we investigate some convergence results of the gradient based iterative algorithm for solving the Sylvester matrix equation A X + X B = C. We first review the development of the gradient based iterative algorithm and correct mistakes of some convergence results. Then, the idea of "two iterative factors" is applied to propose some new relaxed gradient iterative algorithms to improve the convergence performance of their previous algorithms. Finally, some numerical examples are taken to illustrate the correctness of the concluded results. [ABSTRACT FROM AUTHOR]

A Venturi tube is typically used as the flow control element of hybrid rocket motors (HRMs) for the precise control of the flow of a liquid oxidizer, e.g., nitrous oxide, which can efficiently realize the thrust throttling of motors. The flow rate of nitrous oxide in the venturi tube was calculated for a heterogeneous non-equilibrium flow by considering the influence of the variation in the compressibility temperature. In addition, a nitrous-oxide flow rate measurement system was established to calibrate the flow rate coefficient and obtain the corresponding calculation parameters. Results revealed that upstream pressure is not affected by back pressure when the throat pressure is less than a saturated vapor pressure of 4.2 MPa at the local temperature for the venturi tube with a throat diameter of 1.65 mm; however, cavitation disappeared at a back pressure of greater than 6.35 MPa. A flow rate evaluation error of less than 6 wt% was achieved by using the experimentally determined non-equilibrium parameter in the flow rate calculation method.

Herein we have developed a reversible hydrogen–deuterium exchange reaction of nonactivated olefins. By using EtOCS2K as a mediator, the H/D exchange reaction was realized through repeated addition and elimination reactions, demonstrating reversible H/D exchange between ordinary olefins and deuterated olefins. Using the lowest cost D2O without precious metal catalysts and ligands, a broad spectrum of compatibility of functional groups was achieved.

The paper is indicated to constructing a modified conjugate gradient iterative (MCG) algorithm to solve the generalized periodic multiple coupled Sylvester matrix equations. It can be proved that the proposed approach can find the solution within finite iteration steps in the absence of round-off errors. Furthermore, we provide a method for choosing the initial matrices to obtain the least Frobenius norm solution of the system. Some numerical examples are illustrated to show the performance of the proposed approach and its superiority over the existing method CG. [ABSTRACT FROM AUTHOR]

Objective: Cannabinoid CB2 receptors (CB2Rs) are mainly present on immune cells including mast cells, which participate in 2,4-dinitrofluorobenzene (DNFB)-induced allergic contact dermatitis (ACD). In this study, we aimed to investigate whether inhibition of mast cell degranulation was involved in the anti-ACD effect of electroacupuncture (EA) at ST36 via CB2R.Methods: Sprague-Dawley rats were sensitised and challenged with DNFB following EA stimulation for 1 week. Ear swelling, serum IgE levels, local cytokine production and mast cell infiltration were evaluated. Additionally, rat peritoneal mast cells (RPMCs) were isolated and cultured for detection of CB2R expression, mitogen-activated protein kinase (MAPK) signalling activation and mast cell degranulation (including β-hexosaminidase and histamine release) in the presence or absence of CB2R antagonists.Results: EA treatment inhibited ear swelling, suppressed IgE and cytokine production, decreased the number of mast cells and curbed mast cell degranulation, which was associated with the inhibition of p38 phosphorylation in DNFB-induced ACD. Importantly, EA enhanced the expression of CB2R mRNA and protein in the RPMCs. CB2R antagonist AM630 but not CB1R antagonist AM251 effectively reversed the suppressive effect of EA on p38 activation, mast cell infiltration and degranulation.Conclusion: These findings provide more evidence to support the hypothesis that EA promotes CB2R expression in mast cells, which is followed by inhibition of the p38 MAPK pathway, potentially resulting in the anti-ACD effect of EA. This suggests that EA at ST36 may be an effective candidate therapy for treating inflammatory skin diseases such as ACD.

Patients with recurrent intermediate-stage hepatocellular carcinoma and microvascular invasion–positive status benefit from longer survival times by using a combined treatment of sorafenib with transarterial chemoembolization (TACE) compared with TACE alone.

Pitaya contains various types of polyphenols, flavonoid and vitamins which are beneficial for health and it is among the most important commercial tropical fruits worldwide. Endophytic bacteria might be beneficial for plant growth and yield. However, bacterial diversity in pitaya is poorly characterized. In this study, fruits of white and red pitayas from three different origins (Thailand, Vietnam and China) were chosen for endophytic bacteria diversity investigation by using Illumina HiSeq second-generation high-throughput sequencing technology. Large number of endophytic bacteria were detected and 22 phyla, 56 classes, 81 orders, 122 families and 159 genera were identified. Endophytic bacteria diversity was uneven among pitaya fruits from different origins and bacteria structure was different between white pitaya group and red pitaya group. Phylum Bacteroidetes, classes Bacteroidia and Coriobacteriia, orders Bacteroidales and Coriobacteriales, families Prevotellaceae, Bacteroidaceae, Ruminococcaceae, Paraprevotellaceae, Rikenellaceae, Alcaligenaceae and Coriobacteriaceae, genera Prevotella, Bacteroides, Roseburia, Faecalibacteriumand Sutterellawere statistically significant different species (P < 0.05) between white and red pitayas. These findings might be useful for growth improvement, fruit preservation and processing of different pitaya species from different origins.

ABSTRACTThe complete coding sequence of one tobacco (Nicotiana tabacum) gene, ascorbate oxidase (AO), was isolated by the application of reverse transcription-polymerase chain reaction. The tobacco AO gene consists of a 1722-bp open reading frame and encodes a protein of 573 amino acids. Sequence comparison analysis revealed that the tobacco AO protein shares high homology with the AO proteins of Lycopersicon esculentum(89%), Populus trichocarpa(75%), soybean (74%), castor bean (73%) and peach (73%). The prediction of transmembrane helices showed that tobacco AO might be a transmembrane protein. The expression profile was studied and the results indicated that the tobacco AO gene was diversely expressed in different tobacco tissues, including leaves, stem, roots and flowers. Our experiment laid the grounds for further research on this tobacco gene.

PC6 is a classic acupuncture point in traditional Chinese medicine. It is considered to be effective when treating cardiovascular disorders. In the present review the authors have focused on the neurophysiological bases of the effects of PC6 stimulation on cardiovascular mechanisms. Experimental studies have shown that the hypothalamic rostral ventrolateral medulla, arcuate nucleus and ventrolateral periaqueductal gray are involved in acupuncture attenuation of sympathoexcitatory cardiovascular reflex responses. This long-loop pathway also appears to contribute to the long-lasting, acupuncture-mediated attenuation of sympathetic premotor outflow and excitatory cardiovascular reflex responses. Acupuncture of PC6 modulates the activity in the cardiovascular system, an effect that may be attributed to attenuation of sympathoexcitatory cardiovascular reflex responses.

A protocol was described for obtaining a variety of substituted thiophenes with functional potential viametal-free dehydration and sulfur cyclization of alkynols with elemental sulfur (S8) or EtOCS2K in moderate-to-good yields. The method provides the base-free generation of a trisulfur radical anion (S3•–) and its addition to alkynes as an initiator. This research broadens the applications of S3•–in the synthesis of sulfur-containing heterocycles.