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The article is devoted to the identification and characterization of the main tools and mechanisms for the implementation of Chinese cultural diplomacy in relation to the Russian Federation. The author proceeds from the fact that in the context of the new international situation and a new stage in the development of Sino-Russian relations, the maintenance and intensification of cultural exchanges between the two countries plays an important role. The article emphasizes that the means by which China conducts cultural diplomacy in Russia differ in ways and mechanisms depending on the sphere. It is shown that China's cultural diplomacy in Russia has achieved more significant results mainly in the field of education, literature, artistic cooperation and exchange between the media. Strategic, fateful decisions taken by the top political leadership of the two countries aimed at uniting the two states should be understood and accepted by the population of these countries, which is impossible without a deep mutual understanding of cultural heritage and cultural features. In the article, the author gives numerous examples demonstrating the variety of tools and mechanisms used by Beijing to implement cultural diplomacy in relation to Russia. The author concludes that China uses cultural diplomacy as a means of spreading its culture in Russia, effectively presenting the image of modern China. Cooperation between China and Russia in the field of culture is of great strategic importance for the development of relations between the two countries.

We report an improved total synthesis of 4,5-dibromo-9,10-dihydrophenanthrene-2,3,6,7-tetraol, (±)-polysiphenol, via intermolecular McMurray dimerization of 5-bromovanillin and subsequent intramolecular oxidative coupling as the key steps. The synthetic route is applicable to 4,5-dichloro- and 4,5-difluoro-halologues (as well as a 4,5-dialkyl-analogue). Distinctive AA′BB′ multiplets in their 1H NMR spectra for the dimethylene bridges of the dibromo and dichloro compounds reveal them to be room-temperature stable atropisomers, while for the difluoro compound they present as a singlet. X-ray crystal structure determinations of their tetramethylated synthetic precursors show atropisomeric twist angles of 48°, 46°, and 32°, respectively, with the former representing the largest yet observed in any 4,5-disubstituted-9,10-dihydrophenanthrene. DFT computational studies reveal an unprecedented two-stage atropisomeric interconversion process involving time-independent asynchronous rotations of the dimethylene bridge and the biaryl axis for halologues containing chlorine or bromine, but a more synchronous rotation for the difluoro analogue.

Recently, the research on ad-hoc microphone arrays with deep learning has drawn much attention, especially in speech enhancement and separation. Because an ad-hoc microphone array may cover such a large area that multiple speakers may locate far apart and talk independently, target-dependent speech separation, which aims to extract a target speaker from a mixed speech, is important for extracting and tracing a specific speaker in the ad-hoc array. However, this technique has not been explored yet. In this paper, we propose deep ad-hoc beamforming based on speaker extraction, which is to our knowledge the first work for target-dependent speech separation based on ad-hoc microphone arrays and deep learning. The algorithm contains three components. First, we propose a supervised channel selection framework based on speaker extraction, where the estimated utterance-level SNRs of the target speech are used as the basis for the channel selection. Second, we apply the selected channels to a deep learning based MVDR algorithm, where a single-channel speaker extraction algorithm is applied to each selected channel for estimating the mask of the target speech. We conducted an extensive experiment on a WSJ0-adhoc corpus. Experimental results demonstrate the effectiveness of the proposed method.

Since gateway meters cannot simultaneously realize online monitoring and remote verification, the study is aimed at exploring the intelligent online monitoring and remote verification of the gateway meter. First, the similarity and related evaluation indexes of embedded sensors are analyzed based on the relevant theories, like the theories of the embedded sensor and clustering algorithm. Second, the gateway meter is tested on the standard vibration test bed, and the accuracy, timeliness, and environmental adaptability of its intelligent online system are tested. Finally, the remote verification of the gateway meter is carried out from three aspects: the error/load value, secondary voltage drop, and the admittance test. The results show that when the intelligent online monitoring ability of the gateway meter is tested on the standard vibration test bed, the error of the same pilot is controlled between 0.1 mm after multiple peak tests, and the error is within the allowable range. In the intelligent online monitoring system based on the embedded sensor and clustering algorithm, the vibration acceleration is -0.6 cm/s~0.3 cm/s, the speed is -1 cm/s~1 cm/s, and the displacement fluctuates between -0.8 cm/s~0.8 cm/s. This shows that the intelligent online monitoring system can meet the performance requirements of online monitoring. In the process of remote verification of gateway meters, the active error and reactive error are 0.2% mm. The results of the secondary voltage drop and the admittance test show that the relevant technical indexes of the system meet the expected requirements. Therefore, the intelligent online monitoring and remote verification of gateway meters are discussed based on the embedded sensor and clustering algorithm, which provides a reference for the rapid development of gateway meters.

The trough girder with corrugated steel webs (TGCW), introduced to overcome the inherent limitations of traditional trough bridges, is an innovative steel-concrete composite bridge form co...

In this paper, finite element (FE) analysis is carried out to investigate the behavior and strength of multiplanar longitudinal plate-to-circular hollow section (CHS) connections reinforced by external ring stiffeners (abbreviated as MCE connections) under in-plane and out-of-plane bending moments. Local plastic deformation and axial local buckling are observed from the study. Further analysis reveals that the ring stiffener can achieve effective strengthening for such connections and the failure modes are affected by the size of the ring. The capacity of the stiffened connection is increased by three times larger than that of the unstiffened connection, and the failure mode alters from local plastic deformation to axial buckling with the increase of the dimension of the ring. Parametric study is performed to cover a wide range of parameters. The study involves 364 numerical modes with different parameters such as: chord diameter-to-thickness ratio (2γ), ring stiffener width-to-chord diameter ratio (β), ring stiffener thickness-to-chord diameter ratio (η) and chord utilization ratio (n). Accordingly, a set of design formulas is proposed using nonlinear regression analysis, and the formula is compared with the FE results. The results reveal that the proposed formula has practical significance and can provide references for the design of similar connections Refereed/Peer-reviewed

Based on the structural advantages of trough beam and corrugated steel web, considering the capacity, durability and rapid construction of composite structure, a new type of composite structure beam is proposed in this paper, namely Composite Trough Beam with Corrugated Steel Web wrapped with Steel Plate (CTBCS). A scale experimental CTBCS was designed and fabricated to study the bending properties of failure mode, load-displacement curve and section strain distribution of CTBCS. In addition, the calculation method of bending capacity of CTBCS was proposed, and the effects of the thickness of steel plate, the diameter of steel bar in upper flange concrete slab and the height to span ratio on the bending properties of CTBCS were studied. The study in this paper provides reference values for the experimental scheme formulation of composite trough beam, and lays a foundation for the design and application of CTBCS.

BACKGROUND: It has been reported that atractylodin has a potential antitumor effect. This study aimed to investigate the effects of atractylodin on Huh7 and Hccm hepatocellular carcinoma (HCC) cells and its molecular mechanism. METHODS: Huh7 and Hccm cells were cultured in vitro, and their viability was detected by CCK-8 assay and the half inhibitory concentration (IC50) was calculated. The cells were treated with different concentrations of atractylodin, and the migration and invasion ability of cells was detected by scratch assay and Transwell assay. The cell cycle change and apoptosis rate were detected by flow cytometry. IlluminaHiSeq4000 platform was used for transcriptome sequencing, and the results were analyzed for gene differential expression, gene function, and signal pathway enrichment. Morphological changes of cells were detected by transmission electron microscopy, reactive oxygen species (ROS) levels were detected by DCFH-DA probe, and the expressions of ferroptosis related proteins GPX4, ACSL4, FTL, and TFR1 were detected by Western blot. RESULTS: The results showed that atractylodin could inhibit the proliferation, migration, and invasion of Huh7 and Hccm cells, regulate the cell cycle, and induce cell apoptosis and G1 phase cell cycle arrest. In addition, it could significantly induce the increase of intracellular ROS levels, decrease the expression of GPX4 and FTL proteins, and up-regulate the expression of ACSL4 and TFR1 proteins. CONCLUSIONS: Atractylodin can inhibit the proliferation, migration, and invasion of Huh7 and Hccm liver cancer cells, and induce cell apoptosis and cell cycle arrest. In addition, our results suggest that atractylodin may induce ferroptosis in HCC cells by inhibiting the expression of GPX4 and FTL proteins, and up-regulating the expression of ACSL4 and TFR1 proteins.

Recently, supervised speaker-independent speech separation methods, such as deep clustering and permutation invariant training, have demonstrated better performance than conventional unsupervised speech separation methods. However, their performance drops sharply in reverberant environments. To solve the problem, we propose a multi-channel speech separation algorithm that fully explores spatial information. It first extracts a spatial feature, named interaural phase difference (IPD), as one of the input features of the single-channel deep clustering algorithm. Then, it uses the deep clustering as the noise estimation component of the deep-learning-based beamforming. The novelty of the proposed algorithm lies in that it extends the spatial-feature-based deep clustering to a multichannel algorithm which boosts the performance by exploring spatial information at both the input and output of deep clustering. Its advantages have two aspects. First, the spatial feature IPD significantly improves the robustness of deep clustering in reverberant environments. Second, the deep-clusteing-based beamforming, which is a linear algorithm, suffers less nonlinear distortions than the single-channel deep clustering. We have compared the proposed algorithm with the single-channel deep clustering algorithm, spatial-feature-based multi-channel deep clustering with IPD, and deep-clustering-based beamforming without IPD in reverberant environments. Experimental results show that the proposed algorithm performs significantly better than the comparison methods.

Nowadays, more and more NVMe SSDs (PCIe SSDs accessed by NVMe protocol) are deployed and virtualized by cloud providers to improve the I/O experience in virtual machines rent by tenants. Though the IOPS and latency for read and write on NVMe SSDs are greatly improved, it seems that the existing software cannot efficiently explore the abilities of those NVMe SSDs, and it is even worse on virtualized platform. There is long I/O stack for applications to access NVMe SSDs in guest VMs, and the overhead of which can be divided into three parts, i.e., (1) I/O execution on emulated NVMe device in guest operating system (OS); (2) Context switch (e.g., VM_Exit) and data movement overhead between guest OS and host OS; (3) I/O execution overhead in host OS on physical NVMe SSDs. To address the long I/O stack issue, we propose SPDK-vhost-NVMe, an I/O service target relying on user space NVMe drivers, which can collaborate with hypervisor to accelerate NVMe I/Os inside VMs. Generally our approach eliminates the unnecessary VM_Exit overhead and also shrinks the I/O execution stack in host OS. Leveraged by SPDK-vhost-NVMe, the performance of storage I/Os in guest OS can be improved. Compared with QEMU native NVMe emulation solution, the best solution SPDK-vhost NVMe has 6X improvement in IOPS and 70% reduction in latency for some read workloads generated by FIO. Also spdk-vhost-NVMe has 5X performance improvement with some db_benchmark test cases (e.g., random read) on RocksDB. Even compared with other optimized SPDK vhost-scsi and vhost-blk solutions, SPDK-vhost-NVMe is also competitive in per core performance aspect.

There is strong demand on building high performance storage service upon emerging fast storage devices (e.g., NVMe SSDs). Unfortunately, current storage software stack cannot satisfy such requirements and the software overhead becomes a major bottleneck for developing high performance storage applications.According to our performance profiling results, most storage software overhead is caused by kernel I/O stacks due to context switch, data copy, interrupt, resource synchronization and etc. To address these issues, we provide SPDK (storage performance development kit), a set of tools and libraries for writing high performance, scalable, user-mode storage applications. It achieves high performance by moving the necessary drivers into user space and operating them in a polled mode instead of interrupt mode, which eliminates kernel context switch and interrupt handling overhead and also provides lockless resource access. Integrated with SPDK, performance competitive storage applications can be build upon those fast storage devices. In our experiments, the per cpu core IOPS of NVMe device driver in SPDK is about 6X to 10X better than the kernel NVMe driver. Some storage applications are also developed upon SPDK's framework and obtain much better per-core performance compared with the existing solutions. For example, SPDK NVMe over fabrics (NVMe-oF) target is 10X more efficient than Linux Kernel NVMe-oF target solution.

In virtualized environments, the customers who purchase virtual machines (VMs) from a third-party cloud would expect that their VMs run in an isolated manner. However, the performance of a VM can be negatively affected by co-resident VMs. In this paper, the authors propose vExplorer, a distributed VM I/O performance measurement and analysis framework, where one can use a set of representative I/O operations to identify the I/O scheduling characteristics within a hypervisor, and potentially leverage this knowledge to carry out I/O based performance attacks to slow down the execution of the target VMs. The authors evaluate their prototype on both Xen and VMware platforms with four server benchmarks and show that vExplorer is practical and effective. The authors also conduct similar tests on Amazon’s EC2 platform and successfully slow down the performance of target VMs.

In virtualized environments, the customers who purchase virtual machines (VMs) from a third-party cloud would expect that their VMs run in an isolated manner. However, the performance of a VM can be negatively affected by co-resident VMs. In this paper, we propose vExplorer, a distributed VM I/O performance measurement and analysis framework, where one can use a set of representative I/O operations to identify the I/O scheduling characteristics within a hypervisor; and potentially leverage this knowledge to carry out I/O based performance attacks to slow down the execution of the target VMs. We evaluate our prototype on both Xen and VMware platforms with four server benchmarks and show that vExplorer is practical and effective. We also conduct similar tests on Amazon's EC2 platform and successfully slow down the performance of target VMs.