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Osteoporosis is a commonly diagnosed metabolic bone disease. NLRP3 inflammasome activation and pyroptosis are observed during osteoporosis. However, the mechanism by which NLRP3-mediated pyroptosis contributes to osteoporosis remains largely undefined.

Due to simplicity, K-means has become a widely used clustering method. However, its clustering result is seriously affected by the initial centers and the allocation strategy makes it hard to identify manifold clusters. Many improved K-means are proposed to accelerate it and improve the quality of initialize cluster centers, but few researchers pay attention to the shortcoming of K-means in discovering arbitrary-shaped clusters. Using graph distance (GD) to measure the dissimilarity between objects is a good way to solve this problem, but computing the GD is time-consuming. Inspired by the idea that granular ball uses a ball to represent the local data, we select representatives from a local neighborhood, called natural density peaks (NDPs). On the basis of NDPs, we propose a novel K-means algorithm for identifying arbitrary-shaped clusters, called NDP-Kmeans. It defines neighbor-based distance between NDPs and takes advantage of the neighbor-based distance to compute the GD between NDPs. Afterward, an improved K-means with high-quality initial centers and GD is used to cluster NDPs. Finally, each remaining object is assigned according to its representative. The experimental results show that our algorithms can not only recognize spherical clusters but also manifold clusters. Therefore, NDP-Kmeans has more advantages in detecting arbitrary-shaped clusters than other excellent algorithms.

The vision for 6G networks is to offer pervasive intelligence and internet of intelligence, in which the networks natively support artificial intelligence (AI), empower smart applications and scenarios in various fields, and create a “ubiquitous-intelligence” world. In this vision, the traditional session-oriented architecture cannot achieve flexible per-user customization, ultimate performance, security and reliability required by future AI services.

The development of technology that enables network convergence and the rising acceptance of heterogeneous network architectures have made it possible for many of the most important cognitive radio networks to communicate with a broad variety of authorized networks. Traditional algorithms for network selection make use of selection approaches that are dependent on prior information about the network under consideration. We use the proposed algorithm to choose different networks and integrate them into computers. This paper offers a network selection technique that is based on deep reinforcement learning. This technique can be applied to cognitive radio networks that encompass a range of networks. We will utilize these findings to provide recommendations on how to remediate existing issues in the delivery of services to cognitive users. This paper aims to improve the service quality for cognitive users and find solutions to the identified problems.

Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to dynamically change their configurations, such as CPU frequency, computation model size, and image offloading frequency based on user preferences, camera sampling rates, and available radio resources. Our proposed dynamic MAR configuration adaptations can minimize the per frame energy consumption of multiple MAR clients without degrading their preferred MAR performance metrics, such as latency and detection accuracy. To thoroughly analyze the interactions among MAR configurations, user preferences, camera sampling rate, and energy consumption, we propose, to the best of our knowledge, the first comprehensive analytical energy model for MAR devices. Based on the proposed analytical model, we design a LEAF optimization algorithm to guide the MAR configuration adaptation and server radio resource allocation. An image offloading frequency orchestrator, coordinating with the LEAF, is developed to adaptively regulate the edge-based object detection invocations and to further improve the energy efficiency of MAR devices. Extensive evaluations are conducted to validate the performance of the proposed analytical model and algorithms.

Highly-dynamic (HD) map is an indispensable building block in the future of autonomous driving, allowing for fine-grained environmental awareness, precise localization, and route planning. However, since HD maps include rich, multidimensional information, the volume of HD map data is substantial and cannot be transmitted frequently by several vehicles over vehicular networks in real-time. Therefore, in this paper, we propose a data source selection scheme for effective HD map transmissions in vehicular named data networking (NDN) scenarios. To achieve our goal, we created a vehicular NDN environment for data collection, processing, and transmission using the CARLA simulator and robot operating system 2 (ROS2). Next, due to our vehicular NDN’s dynamic and complex nature, we formulate the data source selection problem as a Markov decision process (MDP) and solve it using a reinforcement learning approach. For simplicity, we termed our proposed scheme data source optimization with reinforcement learning (DSORL), which selects suitable vehicles for HD map data transmission to MEC servers. The experiment results indicate that our suggested method outperformed existing baseline schemes, such as RLSS, Pro-RTT, and HDM-RTT, across all performance criteria in the evaluation. For instance, the system throughput increases by $65\%-72.68\%$ compared to other baseline systems. Similarly, the proposed approach can minimize packet loss rate, data size, and transmission time by up to 60.6%, 77.5%, and 54.1%, respectively.

In order to solve the problem that the traditional spectral clustering algorithm is time-consuming and resource consuming when applied to large-scale data, resulting in poor clustering effect or even unable to cluster, this paper proposes a spectral clustering algorithm based on granular-ball(GBSC). The algorithm changes the construction method of the similarity matrix. Based on granular-ball, the size of the similarity matrix is greatly reduced, and the construction of the similarity matrix is more reasonable. Experimental results show that the proposed algorithm achieves better speedup ratio, less memory consumption and stronger anti noise performance while achieving similar clustering results to the traditional spectral clustering algorithm. Suppose the number of granular-balls is $m$m, $n$n is the number of points in the dataset, and $m< < n$m<<n, the time complexity of GBSC is $O(m^{3})$O(m3). It is proved that GBSC has good adaptability to large-scale datasets. All codes have been released at https://github.com/xjnine/GBSC.

The lateral roots of the Aconitum carmichaelii ("Fuzi") have been used for centuries as a cardiotonic in China. The diterpenoid alkaloid talatisamine (TA) is a major bioactive component of Fuzi, but the identity and bioactivities of the TA metabolites have not been examined in detail. In this study, metabolite profiling of TA was performed in rat heart by UPLC-MS following oral administration. Metabolites were identified by comparing protonated molecules, fragmentation patterns, and chromatographic behaviors with those of standard compounds. Metabolites of TA were then prepared and tested for cardiotonic activity on isolated frog hearts. The metabolite cammaconine, a C19 diterpenoid alkaloid with a hydroxyl group at C-18, exhibited substantial cardiotonic activity during frog heart perfusion. To further investigate the structure–cardiac effect relationships, a series of C19-diterpenoid alkaloids with 18-OH were prepared. Eight tested compounds (5 – 12) demonstrated measurable cardioactivity, of which compound 5 with an N -methyl group and compound 7 with a methoxy at C-16 showed stronger effects on ventricular contraction than the other compounds. Thus, 18-OH is a critical structural feature determining cardiotonic activity, and efficacy is improved by the presence of N -methyl or methoxy at C-16. Preliminary mechanistic studies suggested that the cardiotonic effect of compound 5 is mediated by enhanced cellular calcium influx. Metabolites of TA with these structural features may be useful therapeutics to prevent heart failure. [ABSTRACT FROM AUTHOR]

The emerging vehicular connected applications, such as cooperative automated driving and intersection collision warning, show great potentials to improve the driving safety, where vehicles can share the data collected by a variety of on-board sensors with surrounding vehicles and roadside infrastructures. Transmitting and processing this huge amount of sensory data introduces new challenges for automotive edge computing with traditional wireless communication networks. In this work, we address the problem of traditional asymmetrical network resource allocation for uplink and downlink connections that can significantly degrade the performance of vehicular connected applications. An end-to-end automotive edge networking system, FAIR, is proposed to provide fast, scalable, and impartial connected services for intelligent vehicles with edge computing, which can be applied to any traffic scenes and road topology. The core of FAIR is our proposed symmetrical network resource allocation algorithm deployed at edge servers and service adaptation algorithm equipped on intelligent vehicles. Extensive simulations are conducted to validate our proposed FAIR by leveraging real-world traffic dataset. Simulation results demonstrate that FAIR outperforms existing solutions in a variety of traffic scenes and road topology.

High definition (HD) map is a key technology that enables autonomous driving, which has the characteristic of frequent updates and low delay requirements. In order to minimize the HD map updating latency, this paper jointly considers the generation and transmission processes of HD map. The generation process of HD map relies heavily on the data captured by different types of sensors, which is modeled by the federated analytics process. Also, in order to quantify the diversity of utilized sensors, the concept of love of variety is introduced in this paper. Then, considering HD map is composed of dynamic and static layers that have different latency requirements during the transmission process, this paper proposes a method to allocate edge server capacity to each HD map layer such that the overall information staleness can be minimized. Firstly, the deterministic edge capacity case is discussed and the solution is derived by obtaining the Karush-Kuhn-Tucker conditions. Then, considering the practice that an edge server provides services to multiple attached devices simultaneously, contentions among these devices make available capacity for the autonomous vehicle variational. Therefore, an uncertain edge capacity case is discussed as well, where the uncertainty is described by Wasserstein metrics and the problem is reformulated into distributional robust chance constrained optimization problem. And for the low latency purpose, we utilize an inner approximation to reduce the complexity of the original problem and find a suboptimal solution. Finally, simulation results demonstrate the effectiveness of our proposed methods that achieves the lowest latency for HD map updating.

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named LiveMap to detect, match, and track objects on the road. We design the data plane of LiveMap to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We design the control plane of LiveMap to achieve adaptive vehicular offloading with two new algorithms (central and distributed) to balance the latency and coverage performance based on deep reinforcement learning techniques. We conduct extensive evaluation through both realistic experiments on a small-scale physical testbed and network simulations on an edge network simulator. The results suggest that LiveMap significantly outperforms existing solutions in terms of latency, coverage, and accuracy.

Emerging connected vehicular services, such as intelligent driving and high-definition (HD) map, are gaining increasing interest with the fast development of multi-access edge computing (MEC). For most time-sensitive and computation-intensive vehicular services, the data offloading process significantly influences the capacity and performance of MEC, especially when the number of connected vehicles is enormous. In this work, we consider data offloading optimization for a large-scale automotive MEC network. The problem is challenging due to the large number of connected vehicles and the complicated interaction between vehicles and edge servers. To tackle the scalability problem, we reformulate the original offloading optimization problem into a Mean-Field-Game (MFG) problem by abstracting the interaction among the connected vehicles as a distribution over their state spaces of task sizes, known as the mean-field term. To solve the problem efficiently, we propose a G-prox Primal-Dual-Hybrid-Gradient (PDHG) algorithm that transforms the MFG problem into a saddle-point problem. Based on our developed MFG model and G-prox PDHG algorithm, we propose the first data offloading scheme whose computation time is independent of the number of connected vehicles in automotive MEC systems. Extensive evaluation results corroborate the superior performance of our proposed scheme compared with the state-of-the-art methods.

To address the relationships between the amount of nitrogen fertilizer application and the yield of double cropping rice systems, we investigated the effects of a cultivation pattern of strong seedlings with increased planting density and reduced nitrogen application (SDN) on the morphological and physiological characteristics of double cropping rice. Our results indicated that the effects of SDN on the morphological characteristics of the single plant roots of double cropping rice were not significant, but the morphological characteristics of the population roots were largely different. Specifically, SDN significantly increased the morphological indexes of the root population such as root fresh weight, root volume, root number, root length and root dry weight. The effects of SDN on the total root absorption areas and root active absorption areas of the single plants were non-significant, but it dramatically enhanced the total root absorption areas and root active absorption areas of the plant population during the tillering, heading and mature stages. In addition, SDN significantly increased the root bleeding intensity and elevated the soluble sugar and free amino acid contents of root bleeding sap. Compared to the traditional cultivation pattern (CK), SDN significantly increased root bleeding intensity at the heading stage by 4.37 and 8.90% for early and late rice, respectively. Meanwhile, SDN profoundly enhanced the soluble sugar contents of root bleeding sap by 12.85 and 10.41% for early and late rice, respectively. In addition, SDN also significantly enhanced free amino acid content of root bleeding sap by 43.25% for early rice and by 37.50% for late rice systems compared to CK. Furthermore, SDN increased the actual yield of double cropping rice mainly due to the higher effective panicle number and the larger seed-setting rate. The actual yields of early rice under SDN were higher than CK by 9.37 and 5.98% in 2016 and 2017, and the actual yields of late rice under SDN were higher than CK by 0.20 and 1.41% in 2016 and 2017, respectively. Correlation analysis indicated that the significant positive correlations were observed between the majority of the root indexes and the actual yield across the four different growth stages.

Objective: Diffuse large B-cell lymphoma (DLBCL) is an aggressive type of non-Hodgkin lymphoma. Due to its genetic heterogeneity and abnormal metabolism, many DLBCL patients have a poor prognosis. This study investigated the key metabolism-related genes and potential mechanisms. Methods: Differentially expressed genes, differentially expressed transcription factors (TFs), and differentially expressed metabolism-related genes (DEMRGs) of glucose and lipid metabolic processes were identified using the edgeR package. Key DEMRGs were screened by Lasso regression, and a prediction model was constructed. The cell type identification by estimating relative subsets of RNA transcripts algorithm was utilized to assess the fraction of immune cells, and Gene Set Enrichment Analysis was used to determine immune-related pathways. A regulatory network was constructed with significant co-expression interactions among TFs, DEMRGs, immune cells/pathways, and hallmark pathways. Results: A total of 1551 DEMRGs were identified. A prognostic model with a high applicability (area under the curve=0.921) was constructed with 13 DEMRGs. Tumorigenesis of DLBCL was highly related to the neutrophil count. Four DEMRGs (PRXL2AB, CCN1, DECR2 and PHOSPHO1) with 32 TF—DEMRG, 36 DEMRG—pathway, 14 DEMRG—immune-cell, 9 DEMRG—immune-gene-set, and 67 DEMRG—protein-chip interactions were used to construct the regulatory network. Conclusion: We provided a prognostic prediction model based on 13 DEMRGs for DLBCL. We found that phosphatase, orphan 1 (PHOSPHO1) is positively regulated by regulatory factor X5 (RFX5) and mediates MYC proto-oncogene (MYC) targeting the V2 pathway and neutrophils.

Rational nitrogen (N) application can greatly increase rice (Oryza sativaL.) yield. However, excessive N input can lead not only to low N use efficiency (NUE) but also to severe environmental pollution. Reducing N application rate with a higher planting density (RNHD) is recommended to maintain rice yield and improve NUE. The effects of RNHD on fertilizer N fate and rice root growth traits remain unclear. We accordingly conducted a two-year field experiment to investigate the influence of RNHD on rice yield, fertilizer 15N fate, and root growth in a double-rice cropping system in China. In comparison with the conventional practice of high N application with sparse planting, RNHD resulted in similar yield and biomass production as well as plant N uptake. RNHD increased agronomic NUEs by 23.3%–31.9% (P < 0.05) and N recovery efficiency by 17.4%–24.1% (P < 0.05). RNHD increased fertilizer 15N recovery rate by 14.5%–34.7% (P < 0.05), but reduced 15N retention rate by 9.2%–12.0% (P < 0.05). Although a reduced N rate led to significantly reduced root length, surface area, volume, and biomass, these root traits were significantly increased by higher planting density. RNHD did not affect these root morphological traits and reduced activities of nitrate reductase (NR) and glutamine synthetase (GS) only at tillering stage. Plant N uptake was significantly positively correlated with these root traits, but not correlated with NR and GS activities. Together, these findings show that reducing N application with dense planting can lead to high plant N uptake by maintaining rice root growth and thus increase NUE.

Investigate the link between systemic immune-inflammatory index (SII) and Systemic Immune Response Index (SIRI) with osteoarthritis (OA) using National Health and Nutrition Examination Survey (NHANES) data (1999-2018).

Wampee (Clausena lansium) native to southern China, is an important evergreen fruit tree that has a long history of use for medicinal purposes. Here, a chromosome-level genome of C. lansiumwas constructed with a genome size of 282.9 Mb and scaffold N50 of 30.75 Mb. The assembled genome contains 48.70% repetitive elements and 24,381 protein-coding genes. Comparative genomic analysis showed that C. lansiumdiverged from Aurantioideae 15.91-24.95 million years ago. Additionally, some expansive and specific gene families related to methyltransferase activity and S-adenosylmethionine-dependent methyltransferase activity were also identified. Further analysis indicated that N-methyltransferase (NMT) was mainly involved in alkaloid biosynthesis and O-methyltransferase (OMT) participates in the regulation of coumarin accumulation in wampee. This suggested that wampee rich in alkaloids and coumarins might be due to the gene expansions of NMT and OMT. The tandem repeat event was one of the major reasons for NMT expansion. Hence, the reference genome of C. lansiumwill benefit the identification of some useful medicinal compounds from wampee resources and reveal their biosynthetic pathways.

Periodontitis, a chronic inflammatory condition affecting the supporting structures of the teeth, poses a substantial burden on public health and individuals alike. Elevated levels of systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) have been implicated in various inflammatory conditions. This study aimed to investigate the relationship between SII and SIRI with periodontitis.

To analysis the failure and energy absorption of carbon fiber reinforced polymer (CFRP) thin-walled square tube, the quasi-static axial compression loading tests are conducted for [±45]3ssquare tube, and the square tube after test is scanned to further investigate the failure mechanism. Three different finite element models, i.e. single-layer shell model, multi-layer shell model and stacked shell mode, are developed by using the Puck 2000 matrix failure criterion and Yamada Sun fiber failure criterion, and three models are verified and compared according to the experimental energy absorption metrics. The experimental and simulation results show that the failure mode of [±45]3ssquare tube is the local buckling failure mode, and the energy are absorbed mainly by intralaminar and interlaminar delamination, fiber elastic deformation, fiber debonding and fracture, matrix deformation cracking and longitudinal crack propagation. Three different finite element models can reproduce the collapse behaviours of [±45]3ssquare tube to some extent, but the stacked shell model can better reproduce the failure mode, and the difference of specific energy absorption (SEA) is minimum, which shows the numerical simulation results are in better agreement with the test results.

To identify markers associated with in-hospital death in patients with coronavirus disease 2019 (COVID-19)–associated pneumonia.

•MICs of β-lactam antibiotics of non-typeable H. influenzae(NTHi) strains in Western Sichuan, China, were determined.•Of 274 NTHi strains, 141 (51.5%) were β-lactamase-positive, all of which were blaTEM-1type by gene sequencing.•There were 35 amino acid substitutions in the ftsIgene in the NTHi isolates from 2013–2014.•NTHi isolates from 2003–2004 had only nine amino acid substitutions.•Different amino acid substitutions in ftsIhad different effects on β-lactam MICs.

Clustering validation which is applied to the evaluation of clustering results has been recognized as one of the vital issues for clustering application. Density core, a set of connected maximum local density peaks, can approximately represent the structure of a cluster without being affected by noises. Therefore, a density-core-based clustering validation index (DCVI) with minimum spanning tree (MST) is proposed to solve the problem that noises and arbitrary shapes have great influence on the performance of widely used internal validation measures. As for data sets containing arbitrarily shaped clusters with noises, DCVI can effectively obtain the optimal number of clusters. Moreover, experimental results of both synthetic and real data sets indicate that DCVI outperforms most existing measures. [ABSTRACT FROM AUTHOR]

The Fifth Generation (5G) mobile systems have adopted cloud computing and edge computing to support customized services in different application scenarios, such as smart cities, metaverse, interactive Virtual Reality (VR) games, intelligent manufacturing, and autonomous driving. The current 5G network architecture decouples the basic functions of data sensing, communication, and computing at user terminals, mobile networks, and cloud/edge, respectively. Cross-domain resource coordination and service orchestration require in-depth domain knowledge and rich experiences, and hence are very complicated and time-consuming. It is, therefore, very challenging to effectively coordinate heterogeneous resources in distributed facilities for providing agile, stable, and customized services with guaranteed Quality of Experience (QoE) for everyone in dynamic mobile environments.

Exposure to a hygrothermal environment would cause severe degradation of the mechanism properties of carbon fiber reinforced polymer (CFRP) laminates. The moisture absorption characteristics of T700/3228 CFRP laminates was studied through moisture absorption tests. The effects on the tensile and compression properties of [0]16and [90]16specimens under various hygrothermal conditions were comparative analyzed, and the degradation mechanisms of tensile and compression properties were further researched by microstructure characterization. The results show that under dry conditions, matrix strength increases with rising of temperature because of post-curing of epoxy at high temperatures. At 85 % RH (relative humidity), the strength of CFRP laminates decrease entirely with rising temperature because of the thermal and moisture stress generated at the interface. At a water temperature of 70 °C, the proprieties of CFRP laminates decrease severely.

There is increasing evidence for associations between polymorphisms of the oxytocin receptor (OXTR) gene and autism spectrum disorder, but to date no study has established links with autistic traits in healthy subjects and potential cultural differences. The present research firstly investigated associations between three widely studied OXTRSNPs and autistic and empathic traits (rs53576 (G/A); rs2254298 (G/A); rs2268498 (T/C)) in two independent studies on male and female Caucasian (n= 537) and Chinese students (n= 280). Autistic and empathic traits were measured in all subjects in the two independent groups using the Autism ‐Spectrum Quotient (AQ) and the Interpersonal Reactivity Index (IRI) respectively, together with their sub‐scales. For both sites, genotyping of the OXTRSNPs was conducted on buccal swab samples using a Cobas Z 480 Light Cycler following automated DNA extraction. Associations at the genotype level with autism trait scores were found in Caucasian subjects for rs2268498 only, with TT carriers having the lowest AQ scores compared with those carrying at least one C‐allele. This finding was independently replicated in the Chinese sample although a smaller proportion carried the C‐allele compared with the Caucasian sample. Some minor associations were found between empathy trait scores and the three SNPs but were not consistent between the samples. These findings show for the first time that the rs2268498 SNP localized in the promoter flanking region of the OXTRgene is associated with autistic traits in different ethnic/cultural groups. This provides further support for the role of the OXTRgene in relation to autism.

The perovskite prototype is one of the most promising solar cell materials. However, perovskite suffers from a phase transition leading to thermodynamic instability, which tightly influences the solar cell operation performance. Thus, modulating transition dynamics would extend its lifetime, which needs an in-depth understanding of the potential energy surface (PES) and the phase transition kinetics at the atomic level. In this work, taking CsPbI3as an example of a perovskite prototype, we map out the PES and resolve the three lowest energy barrier paths of γ-CsPbI3degradation by using a stochastic surface walking method integrated with high-dimensional neural-network potential. Path I is γ-CsPbI3to hexagonal δ′-CsPbI3, a five-step transition with (110)Pvto (001)hexwith the energy barrier 0.25 eV/f.u.; Path II is γ-CsPbI3to cmcm-CsPbI3, a two-step transition with an over all energy barrier 0.22 eV/f.u. and (001)Pv//(110)cmcm+ [010]Pv//[001]cmcm; Path III is γ-CsPbI3to δ-CsPbI3, a one-step transition without forming an inherent interface, with the highest energy barrier 0.34 eV/f.u. Interestingly, We find that with the substitution of the A-site and/or B-site by other atoms, such as Bi and Te, the γ-CsPbI3to δ-CsPbI3transition could be extensively hindered. In this work, by resolving the potential energy surface, we not only reveal the degradation mechanism at the atomic level but also find a way to design perovskites with high and long-term stability.

ABSTRACTThe lattice Boltzmann method (LBM) is a potent numerical technique based on kinetic theory. It can effectively simulate the steady-state and unsteady-state flow problems. A multi-domain decomposition strategy is developed for the LBM in order to accelerate the rate of convergence in different domains by the relative error in the different regions of the steady-state flow. In addition, the proposed method can effectively reduce the amount of calculations required and improve numerical stability. Numerical experiments involving both two- and three-dimensional steady-state flows demonstrate drastically improved computational efficiency and superior numerical stability over the popular lattice Bathnagar-Gross-Krook (BGK) model. Moreover, for unsteady-state flow problems, the presented method can also be used to obtain the flow phenomenon of some unsteady-state flows by choosing the suitable parameter.

A protein contact map is a simplified, two-dimensional version of the three-dimensional protein structure. Protein contact map is proved to be crucial in forming the three-dimensional structure. Contact map prediction has now become an indispensable and promising intermediate step towards final three-dimensional structure prediction, while directed sequence-structure prediction hits its bottlenecks. In this article, different evaluation scores of prediction efficiency are compared. Next, the state of the art and future perspectives of contact map methods are reviewed and special attention is paid to those relying on machine learning algorithms. Details of neural network based methods as well as a list of machine learning based methods are given. Finally, bottlenecks and potential improvements of contact map predictions are discussed.

The hot flushing is a major means of removing wax. For low pressure reservoir, it is possible to produce a large number of washing fluid flowing into the formation, which affects the well production recovery. The application of hollow sucker rod hot flushing technology can avoid fluid pouring back into the formation, and the wells can keep production. Based on the principle of hollow sucker rod hot flushing and heat transfer theory, calculation model of temperature field was established, and hollow sucker rod hot flushing simulation system was developed, which can guide flushing in oilfield.

Objective: The use of transthoracic echocardiography (TTE) to identify right-sided infective endocarditis (RSIE) vegetation is controversial. Data are scarce for patients with no history of intravenous drug abuse (IVDA) or cardiac device insertion. This study analysed the consistency of presurgical echocardiographic results with surgical findings for vegetation identification, and the factors that influence accuracy of echocardiography.Methods: This retrospective trial divided infective endocarditis (IE) patients into three subgroups according to the results of their presurgical TTE: left-sided native IE (LSNIE), left-sided prosthetic valve IE (LSPIE) and RSIE. The accuracy of TTE was tested by comparing vegetation (number and location), detected presurgery by TTE, with actual findings during surgery.Results: In total, 416 patients were analysed, 322 with LSNIE, 31 with LSPIE and 63 with RSIE. Consistency between TTE findings and surgical results was lower in the RSIE group compared with the LSPIE and LSNIE groups. Consistency was lowered by the presence of vegetation in multiple locations and atypical distribution – both of which were increased in the RSIE group. The chance of vegetation in both sides of the heart rose with increased numbers of vegetation locations in RSIE patients. A high proportion of RSIE patients had congenital heart defects, mostly ventricular septal defects.Conclusions: TTE may be unsuitable for RSIE patients with no history of IVDA or cardiac device insertion, because multifocal and atypically distributed vegetation may influence detection accuracy.

Analyzing similarities and differences between biomolecular networks comparison through website intuitively could be a convenient and effective way for researchers. Although several network comparison visualization tools have been developed, none of them can be integrated into websites. In this paper, a web-based tool kit named dynamically adaptive Visualization of Biomolecular Network Comparison (Bio-NCV) is designed and developed. The proposed tool is based on Cytyoscape.js – a popular open-source library for analyzing and visualizing networks. Bio-NCV integrates arjor.js which including the Barnes-Hut algorithm and the Traer Physics library for processing in order to express the dynamically adaptive initialization. In addition, in order to maintain consistency, the counterparts in other networks will change while the nodes and edges in one of the compared networks change. Furthermore, Bio-NCV can deal with both directed and undirected graphs.

To improve the system efficiency of rod pumped wells is a systematical engineering which depends on various cost control methods during the operational process and technologies of improving equipment & system operational efficiency, and closely related to pumping equipment selection, production parameters optimization design and well management. This paper analyzes the existing problems under current management mode and proposes to establish management control mode where application and of rod pumped well engineering design system software is organically linked with maintenance from the perspective of the user.

By the scanning electron microscopy, the microstructure and mechanical properties testing, the effect of Ca and Y on the microstructure and mechanical properties of magnesium alloy AZ81 are investigated in this paper. The results show that with Ca and Y addition, the grain size is refined. And with the β-Mg17Al12 phase reducing significantly, there will be the precipitation of Al2Ca and Al2Y. Meanwhile, the mechanical properties of AZ81 magnesium alloy are enhanced at room temperature and 150°C.

This study investigates the specific impacts of competition on operational efficiency of China's coastal ports. Firstly, we use Data Envelopment Analysis (DEA) to calculate the scores of operational efficiency of selected China's coastal ports from 2010 to 2019. Findings are as follows: (1) except for an individual port (Lianyungang port), the scores of most ports are overall above 0.8; (2) Ports have significant gaps in scores. Secondly, we use panel data model to analyse the specific effects of competition on operational efficiency, and obtain three notable points: (1) Competition among China's coastal ports is relatively intense from 2010 to 2019; (2) Competition improves the operational efficiency of ports; (3) Port size is used more than the production technology and the level of operation and management to improve operational efficiency before 2014, while the opposite is true after 2014.

The numerical simulation method was adopted to analyze the effect on boundary layer thickness reduction of various vortex generator parameters. Results show that vortex generators are capable of reducing boundary layer thickness for about 66 percent, and the influence on centerline Mach number distributions is neglectable. Practicable vortex generators for 2.4m transonic wind tunnel half-model test section side wall are founded. Research results can be used for further applications of vortex generator in wind tunnel tests.

Tumor bed (TB) change is often seen during the whole breast irradiation (WBI) in early breast cancer patients. The aims of this study were to evaluate change in seroma volume on repeat CTs and to explore whether replanning is necessary in breast conserving radiotherapy (RT) using the intensity modulated radiotherapy (IMRT) with simultaneous integrated boost (SIB). Thirty patients underwent WBI with 84 CT scans (24 with 3 CTs (CT1, CT2 and CT3) and 6 without CT3) during the 6 weeks of RT were reviewed. TB and other target volumes on all CTs were delineated and compared. IMRT-SIB treatment plans with 50.68 Gy to the whole breast and 64.4 Gy to the boost in 28 fractions were constructed in the CT1. Replan and hybrid plan (without replanning) in the CT2 were reproduced. Dosimetric differences between the replans and hybrid plans were also compared. The mean TB volumes for CT1, CT2 and CT3 were 42.1 cm3, 20.1 cm3, 17.0 cm3, respectively. The mean TB volume reduction was 40.5% from the CT1 to CT2 and 4.3% from the CT2 to CT3. The difference of TB volumes between the CT1 and CT2 was statistically significance (p< 0.001), but not significant between the CT2 and CT3 (p= 1.000). For all patients, target coverage remained adequate with either hybrid plans or replans. However, replanning can significantly decrease the whole breast mean dose (35.2 vs. 35.6 Gy, p= 0.026) and breast volume outside the boost receiving 95% of the boost prescribed dose (39.5 vs. 68.2 cm3, p< 0.001). In summary, boost volume could irradiate adequately after the significance shrinkage of tumor bed with the fractionated schedule of IMRT-SIB, but replanning on a second CT could avoid the undesired high dose irradiated breast_tissue outside boost.

The corrosion process of rare earth (RE) SPCC steel and SPCC steel in CSP in 3.5% NaCl solutions was investigated by SEM, EDS, XRD and polarization curve measurements. The results show that grain refinement can be obtained, the inclusions will be formed, and the electrochemical corrosion of microscopic region is weakened and therefore the corrosion resistance of SPCC steel could be improved by addition of RE. The rust layer of RE treated steel generated in the solution is more compact, the α-FeOOH content in rust layer is higher, and the content of activated γ-FeOOH and β-FeOOH is smaller, which are helpful to protect the steel. It also shows that current densities of SPCC steel is lower and the corrosive tendency will decrease with the addition of RE, which also makes the corrosion resistance of steel improved.

The thesis states the building lighting regulated system composition, basic functions and work processes. Based on the compensation principle, making use of transformer controlled voltage source and the controlled current source model, the thesis analyzes the principle of the system voltage. According to the illumination and life span character of the lighting lamps and lanterns in different voltage, the thesis gets the optimal power supply of building lighting system electric lamps and lanterns. Finally, the building indoor lighting using optimal power supply voltage lighting and energy saving rate of building exterior wall using time-varying stabilized voltage control scheme are analyzed and calculated.

Rocky rhodium: The oxidative amidation of aromatic ethers with no substituents in the paraposition catalyzed by rhodium acetate was achieved in moderate yields (see scheme) by using ditrifluoroacetoxyiodobenzene as the oxidant under mild conditions (RT).

The blasting vibration derived from multiple blasts and its effects on surrounding rock in a longspan tunnel will probably give rise to cumulative damage of surrounding rock, and weaken surrounding rock’s stability. Blastmate III, made by the Canadian company Instantel, is utilized to monitor the vibration signals from multiple blasts. Blasting vibration decay law and allowed safety distance of blasting are calculated by using Sadaovsk formula. Then, cumulative effects are analyzed by Hilbert-Huang Transform method as well as consequential instantaneous energy spectrum, and UTEP is presented to indicate the change of cumulative effects and damage in surrouding rock. The research shows that during the progress of damage evolution, “strengthening” and” deteriorating” may alternately appear several times. This is a zigzag reciprocating process in damage evolution of surrounding rock.

This paper investigates the stability issues of functionally graded material (FGM) panels subjected simultaneously to both aerodynamic and thermal loads. Finite Element Method is employed to model the panel structures and the supersonic aerodynamics is calculated by the first-order piston theory. The critical buckling temperature elevation of the panels is at first predicted. The nonlinear static analysis of the panels is then implemented at certain interval of temperature elevation before buckling onset to obtain structural stiffness matrix. The flutter speed of the panels with updated stiffness matrix corresponding to a certain temperature elevation is estimated. The results show that the FGM panels can offer beneficial effects, especially prevention from buckling. However, if FGM panels integrated with TPS are to be applied on supersonic vehicles, one should pay more attention to the boundary conditions to guarantee the dynamic stability.

This paper investigates aeroelastic tailoring and optimal trailing edge control surface deflection to minimize induced drag for a HALE UAV flying wing configuration. The analysis process is conducted on the Finite Element(FE) model of a composite slender wing. Genetic Algorithm(GA) is employed to aeroelastically tailor the wing by setting the composite ply orientation. The study examined conformal and traditional flaps and explored two optimization formulations to minimize drag. The impacts of the conformal control surface are recognized as required deflection saving which can be translated to drag reduction. The results also show that the control demands for the optimal trim can be further reduced if the wing is properly tailored.

Anonymity is an important security aspect of wireless communications and has continuously attracted significant attention. Implementing anonymity of mobile users not only protects their privacy but also reduces the chances of attacks based on impersonation; therefore security can be improved. Untraceability is a related issue to anonymity. If a user is traceable, its hidden identity can be revealed through profiling the activities associated to a user. In this paper, we conduct a survey on anonymity issues of wireless communication systems. We first discuss general issues of anonymity in wireless communication systems. Then we survey some protocols in the literature, which are designed for wireless mobile systems as well as wireless ad hocnetworks. Copyright © 2008 John Wiley & Sons, Ltd.