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Due to the increase in wind power accessing power system in recent years, the low-frequency oscillation of power system is more complicated. Therefore, a new adaptive switching control method combined with a nonlinear system is proposed for the complex operating conditions of the grid-connected power system. Firstly, this method decomposes the low-frequency oscillation control problem of the power system into two parts, which are the linear system control and the nonlinear system control. Then, in the linear system control part, the iterative identification method is used to control this part. In the nonlinear system control part, the nonlinear term is decomposed into a combination of the previous beating measurable and the nonlinear estimation increment. Secondly, the nonlinear system is divided into three different operating conditions by the nonlinear estimation increment. Thus, four different adaptive controllers are obtained. Then, the designed controller is switched according to the controlled object with four controllers by the switching function, which not only ensures the stability of the closed-loop system, but also improves the control performance of the controller under different operating conditions. Finally, the feasibility and effectiveness of this method are verified, and the controller can fit the control efficiency of the real power system well.

The radula is a unique foraging organ to Mollusca, which is important for their evolution and taxonomic classification. Many radulae are mineralized with metals. Although the remarkable mechanical properties of mineralized radulae are well-studied, the formation of mineralization from nonmineralized radulae is poorly understood. Taking advantage of the recently sequenced octopus and chiton genomes, we were able to identify more species-specific radular proteins by proteomics. Comparing these proteomes with the known limpet radula proteome enabled us to gain insight into the molecular components of nonmineralized and mineralized radula, highlighting that iron mineralization in the chiton radula is possibly due to the evolution of ferritins and peroxiredoxins. Through an in vitro binding assay, ferritin is shown to be important to iron accumulation into the nonmineralized radula. Moreover, radular proteomes reflect their adaption to dietary habits to some extent. The octopus radula has many scaffold modification proteins to suit flexibility while the chiton radula has abundant sugar metabolism proteins (e.g., glycosyl hydrolases) to adapt to algae feeding. This study provides a foundation for the understanding of molluscan radula formation and evolution and may inspire the synthesis of iron nanomaterials.

Object detection in traffic scenes has attracted considerable attention from both academia and industry recently. Modern detectors achieve excellent performance under a simple constrained environment while performing poorly under the actual complex and open traffic environment. Therefore, the capability of adapting to new and unseen domains is a key factor for the large-scale application and proliferation of detectors in autonomous driving. To this end, this paper proposes a novel category-induced coarse-to-fine domain adaptation approach (C2FDA) for cross-domain object detection, which consists of three pivotal components: (1) Attention-induced coarse-grained alignment module (ACGA), which strengthens the distribution alignment across disparate domains within the foreground features in category-agnostic way by the minimax optimization between the domain classifier and the backbone feature extractor; (2) Attention-induced feature selection module, which assists the model to emphasize the crucial foreground features and enables the ACGA to focus on the relevant and discriminative foreground features, without being affected by the distribution of inconsequential background features; (3) Category-induced fine-grained alignment module (CFGA), which reduces the domain shift in category-aware way by minimizing the distance of centroids with the same category from different domains and maximizing that of centroids with disparate categories. We evaluate the performance of our approach in various source/target domain pairs and comprehensive results demonstrate that C2FDA significantly outperforms the state-of-the-art on multiple domain adaptation scenarios, i.e., the synthetic-to-real adaptation, the weather adaptation, and the cross camera adaptation.

IntroductionPhysical activity among college students since the COVID-19 pandemic was well studied; however, subjective exercise experience and the emotional response toward physical exercise received less attention.MethodsThe present study used latent profile analysis (LPA) to explore the latent class of subjective exercise experience among physically vulnerable college students who scored 59 points or less in tests under the National Student Physical Health Standard. Three non-duplicated samples at different stages of the COVID-19 pandemic were collected in March 2020 (N = 127), March 2021 (N = 118), and November 2021 (N = 206) respectively. Psychometrically validated scales, namely, Subjective Exercise Experiences Scale (SEES), Generalized Anxiety Disorder (GAD-7), and Patient Health Questionnaire (PHQ-9) were used to measure subjective exercise experience, anxiety symptoms, and depressive symptoms.Results and discussionLPA revealed a 3-class solution for the subjective exercise experience of physically unfit students, namely, the “negative experience group” (30.82%), the “fatigue group” (41.91%), and the “positive experience group” (27.27%). Multinomial regression showed that probable anxiety [odds ratio (OR) = 0.12] was associated with the overall negative exercise experience while probable depression (OR = 0.19) was associated with psychological fatigue. Women (OR = 0.496) were more likely to experience overall negative exercise experience, and the outbreak of the COVID- 19 (OR = 2.14) pandemic influenced the psychological distress of the subjective exercise experience compared with the other two phases in the post-COVID- 19 era. Our findings provided significant implications for physical education targeting university students that interventions should be tailored differently for three profiles of the subjective exercise experience.

Biogas slurry(BS) from food waste anaerobic fermentation coexisted a lot of salinity that could damage soil and crops health. So, this study was to explore the effect of the application of biogas slurry on soil salinization in 1 ∼ 4 cm, 4-6 cm and 6 ∼ 8 cm soil layers every 10 days, Chinese cabbage growth and rhizobacteria. The results indicated that ≤ 10% concentration of biogas slurry was uninjurious for soil and plant, the dry weight growth rate was 73.7% compared with CK, long term application should be further evaluated the potential risk of salinity on underground water and human health. As for high concentration of biogas slurry ≥ 10% concentration of biogas slurry could inhibit the seed germination and root elongation, and the germination percentage was declined from 87.6% to 2.4%, but 50% and 100% concentration of biogas slurry showed a promotion of crop growth because of sufficient nutrition. However, the potential accumulation of salinity could be seen in high concentration of biogas slurry for long term application especially in top1-4 cm soil. Correlation analysis showed that Cl

This study presents a hybrid collaborative filtering recommendation algorithm for sparse data (HCFDS) to increase the recommendation impact by addressing the problem of data sparsity in standard collaborative filtering methods. To begin, the similarity calculation divergence is evident in a data sparse environment due to the difference in user scoring standards and the rise in weight of the same score in the overall score. The user similarity algorithm IU-CS and item similarity algorithm II-CS are suggested in this work by incorporating the score difference threshold and the same score penalty factor, in order to address the deviation of similarity computation caused by the excessive dilation. Second, this work offers a filling optimization technique for score prediction to address the issue of missing score matrix data. The II-CS algorithm presented in this work is used to forecast the missing items in the scoring matrix first, and then, the user’s preference score in the item category dimension is utilized to correct the score prediction value and fill the matrix. Finally, the IU-CS method presented in this work is used in this study to provide recommendations on the filled score matrix. Experiments indicate that, when compared to the preoptimization method and other algorithms, the optimized algorithm successfully solves the problem of data sparsity and the recommendation accuracy is considerably increased.

Cholangiocarcinoma is an extremely malignant cancer with poor prognosis. Finding efficient diagnosis and treatment is the indispensable way to improve the prognosis of CCA patients. Therefore, exploring molecular abnormalities in CCA development is urgently needed. DLEU1 is a potential tumor-related lncRNA and abnormally expressed in multiple cancers. In this study, TCGA data analysis showed upregulation of DLEU1 expression in CCA. Furthermore, we confirmed that DLEU1 expression was increased in CCA tissues and cells compared with corresponding controls. Upregulated DLEU1 was related to poor clinicopathological characteristics. Functionally, silencing DLEU1 inhibited CCA proliferation, invasion, stemness maintenance and chemo-resistance, whereas amplifying DLEU1 promoted malignant biological behavior of CCA cells. Mechanistically, DLEU1 expression was transcriptionally facilitated by transcription factor YY1. Moreover, DLEU1 promoted oncogene YAP1 expression by functioning as a sponge to competitively bind to miR-149-5p. YAP1 promoted CCA proliferation, invasion and stemness maintenance, whereas miR-149-5p inhibited malignant biological behavior of CCA. Rescue experiments confirmed that the cancer-promoting effect of DLEU1 was saved by interfering miR-149-5p or YAP1. Furthermore, YAP1 promoted tumor stemness maintenance partly by acting as a transcriptional coactivator to promote TEAD2-induced SOX2 expression. These findings indicated that YY1-induced DLEU1 played a crucial role in CCA progression via miR-149-5p/YAP1/TEAD2/SOX2 axis.

Geographical traceability is crucial to the quality and safety control of gelatin. However, currently, methods for gelatin traceability have not been established anywhere in the world. This study aimed to investigate the possibility of differentiating the geographical origins of gelatin from different regions in China using stable isotope technology. To achieve this objective, 47 bovine stick bone samples from three different regions (Inner Mongolia, Shandong, and Guangxi, respectively) in China were collected, and gelatin was extracted from these bones using the enzymatic method. The fingerprint characteristics of stable isotopes of δ13C, δ15N, and δ2H of gelatin from different regions in China were studied. Moreover, isotopic changes from the bone to gelatin during the processing were examined to evaluate the effectiveness of these factors as origin indicators. The results of the one-way analysis of variance (ANOVA) showed that the δ13C, δ15N, and δ2H of gelatin from different regions display significant differences, and using the linear discriminant analysis (LDA), the correct differentiation of origin reached 97.9%. Certain differences in stable isotope ratios were observed during the processing of bone to gelatin samples. Nonetheless, the fractionation effect caused by the processing of bone to gelatin samples was not sufficient to influence the identification of gelatin from different origins, which proves that δ13C, δ15N, and δ2H are effective origin indicators of gelatin. In conclusion, the stable isotope ratio analysis combined with the chemometric analysis can be used as a reliable tool for identifying gelatin traceability.

Driven by the vision of "intelligent connection of everything" toward 6G, the collective intelligence of networked machines can be fully exploited to improve system efficiency by shifting the paradigm of wireless communication design from naive maximalist approaches to intelligent value-based approaches. In this article, we propose an on-purpose machine communication framework enabled by joint communication, sensing, and computation (JCSC) technology, which employs machine semantics as the interactive information flow. Naturally, there are potential technical barriers to be solved before the widespread adoption of on-purpose communications, including the conception of machine purpose, fast and concise networking strategy, and semantics-aware information exchange mechanism during the process of task-oriented cooperation. Hence, we discuss enabling technologies complemented by a range of open challenges. The simulation result shows that the proposed framework can significantly reduce networking overhead and improve communication efficiency., Comment: 8 pages, 6 figures

Artificial intelligence-empowered smart things (e.g., robots, autonomous vehicles, and unmanned aerial vehicles) have been transforming the world. The “brains” of smart things can be abstracted as the agents or cybertwins residing on end devices and edge servers. The next-generation communication networks (i.e., 6G) will become the nervous system for these agents and natively support multiagent cooperation. By sharing local observations and intentions via communication channels, the agents could better understand the environments and make right decisions. Due to the limited channel bandwidth, the communication is considered as a bottleneck of multiagent cooperation. In this article, we propose a graph convolutional communication method (GraphComm) for multiagent cooperation to relive the bottleneck. Specifically, a variational information bottleneck is used to encode the observations and intentions compactly. Furthermore, a graph information bottleneck with the attention-based neighbor sampling mechanism is utilized to improve the effectiveness and robustness of the multiround communication process. The experimental results show that GraphComm can improve the effectiveness, robustness, and efficiency of communication in multiagent cooperative tasks as compared to baseline methods.

An NAD+-dependent xylitol dehydrogenase from A. flavus (AfXDH) was cloned and successfully expressed in Escherichia coli. AfXDH gene sequence revealed an open reading frame of 1,110 bp, encoding a polypeptide of 369 amino acids with a calculated molecular mass of 38,893 Da. Among various polyols, sorbitol and xylitol were preferred substrates of AfXDH with Km values of 16.2 and 16.9 mM, respectively. AfXDH showed the highest activity in Tris-glycine-NaOH buffer (pH 9.5) at 50°C; it required Zn2+ or Mn2+ for enzyme activity. The half-life at 40°C and half denaturation temperature (T1/2) was 200 min and 45°C, respectively. Bioinformatic analyses along with biochemical properties confirmed that AfXDH belonged to the medium-chain dehydrogenase/reductase family. AfXDH exhibits higher thermostability and kcat values than those of other XDHs. The feasibility of using AfXDH in l-xylulose production was demonstrated. AfXDH, when coupled with Streptococcus pyogenes NADH oxidase, efficiently converted xylitol to l-xylulose with 97% yield, suggesting its usefulness for the industrial l-xylulose production from xylitol.

Upcycling of carbon dioxide towards fuels and value-added chemicals poses an opportunity to overcome challenges faced by depleting fossil fuels and climate change. Herein, combining highly controllable molecular beam epitaxy growth of gallium nitride (GaN) under a nitrogen-rich atmosphere with subsequent air annealing, a tunable platform of gallium oxynitride (GaN1-xOx) nanowires is built to anchor rhodium (Rh) nanoparticles for carbon dioxide hydrogenation. By correlatively employing various spectroscopic and microscopic characterizations, as well as density functional theory calculations, it is revealed that the engineered oxynitride surface of GaN works in synergy with Rh to achieve a dramatically reduced energy barrier. Meanwhile, the potential-determining step is switched from *COOH formation into *CO desorption. As a result, significantly improved CO activity of 127 mmol‧gcat−1‧h−1 is achieved with high selectivity of >94% at 290 °C under atmospheric pressure, which is three orders of magnitude higher than that of commercial Rh/Al2O3. Furthermore, capitalizing on the high dispersion of the Rh species, the architecture illustrates a decent turnover frequency of 270 mol CO per mol Rh per hour over 9 cycles of operation. This work presents a viable strategy for promoting CO2 refining via surface engineering of an advanced support, in collaboration with a suitable metal cocatalyst.

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Single-layer graphene has been demonstrated to be a high-efficiency hydrogen isotope sieving membrane in the electrochemical hydrogen pumping system. In this work, we transferred this membrane to proton exchange membrane water electrolysis (PEMWE), which has wide industrial applications. Two membrane electrode assemblies with decorated Pt and ink-coated Pt were investigated. The graphene with the decorated Pt scheme acquired the reported highest proton-to-tritium separation factor of 19.50 in PEMWE. However, rather than graphene, the decorated catalyst was demonstrated to be responsible for this remarkable separation efficiency. Previous studies from Geim's group underestimated the enhanced separation efficiency of decorated Pt over ink-coated Pt, resulting in an exaggerated separation efficiency for graphene. The behavior of proton transfer with hydrogen isotope separation through graphene was interpreted by a serial-parallel circuit model, which suggested that hydrogen isotope separation occurs at defect sites. The limited separation efficiency for graphene was also well understood by a density functional theory (DFT) calculation using an SW 55-77 model and the transition state theory for the kinetic isotope effect. This research provides a thorough understanding of proton transfer with hydrogen isotope separation through graphene.

In recent years, global warming has become an important topic of public concern. As one of the most promising carbon capture technologies, solid amine adsorbents have received a lot of attention because of their high adsorption capacity, excellent selectivity, and low energy cost, which is committed to sustainable development. The preparation methods and support materials can influence the thermal stability and adsorption capacity of solid amine adsorbents. As a supporting material, it needs to meet the requirements of high pore volume and abundant hydroxyl groups. Industrial and biomass waste are expected to be a novel and cheap raw material source, contributing both carbon dioxide capture and waste recycling. The applied range of solid amine adsorbents has been widened from flue gas to biogas and ambient air, which require different research focuses, including strengthening the selectivity of CO

Improving the travel efficiency of citizens and the operation efficiency of urban has always been the goal of Intelligent Transportation System. Due to the neglect of strong traffic demand and driving behaviour preferences, coupled with the insufficient of communication and computing power, the existing measures based on the centralized control of vehicles or mandatory traffic restrictions lead to the traffic efficiency dramatically deviates from the system optimum. Which puts forward an urgent demand for multi-vehicle collaborative apportionment, but also brings challenges. In this paper, a double-layer collaborative apportionment method for connected vehicles, 2L-CoV for short, is proposed under the assistance of Space-air-ground integrated networks. 2L-CoV includes the traffic flow scheduling in global-layer and the vehicle routing planning in local-layer. Firstly, a distributed collaborative framework based SAGIN is presented to make a large-scale of virtual vehicles can interact with each other. Then, at the global-layer, traffic flow is guided speedily by an improved back-pressure algorithm to complete traffic flow scheduling; at the local-layer, considering the driving behaviour preferences, a game evolution online learning approach based on dominant strategy is proposed to plan the vehicle routing. Finally, the simulation results show that 2L-CoV can effectively balance the traffic network, improves the network throughput, and reduces the total travel time.

Prodigiosin is a natural red pigment derived primarily from secondary metabolites of microorganisms, especially Serratia marcescens. It can also be chemically synthesized. Prodigiosin has been proven to have antitumor, antibacterial, antimalaria, anti-insect, antialgae, and immunosuppressive activities, and is gaining increasing important in the global market because of its great potential application value in clinical medicine development, environmental treatment, preparation of food additives, and so on. Due to the low efficiency of prodigiosin chemical synthesis, high-level prodigiosin of production by microorganisms are necessary for prodigiosin applications. In this paper, the production of prodigiosin by microorganism in recent decades is reviewed. The methods and strategies for increasing the yield of prodigiosin are discussed from the aspects of medium composition, additives, factors affecting production conditions, strain modification, and fermentation methods.

Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities. As the quantity of intelligent vehicles and the demand for edge computing are increasing rapidly, it becomes critical to efficiently orchestrate the communication and computation resources on edge clouds. Existing methods usually perform resource allocation in a fairly effective but still reactive manner, which is subject to the capacity of nearby edge clouds. To deal with the contradiction between the spatiotemporally varying demands for edge computing and the fixed edge cloud capacity, we proactively balance the edge computing demands across edge clouds by appropriate route planning. In this paper, route planning and resource allocation are jointly optimized to enhance intelligent driving. We propose a multi-scale decentralized optimization method to deal with the curse of dimensionality. In large-scale optimization, backpressure algorithm is used to conduct route planning and load balancing across edge clouds. In small-scale optimization, game-theoretic multi-agent learning is exploited to perform regional resource allocation. The experimental results show that the proposed algorithm outperforms the baseline algorithms which optimize route planning and resource allocation separately.

It is widely recognized that connected vehicles have the potential to further improve the road safety, transportation intelligence and enhance the in-vehicle entertainment. By leveraging the 5G enabled Vehicular Ad hoc NETworks (VANET) technology, which is referred to as 5G-VANET, a flexible software-defined communication can be achieved with ultra-high reliability, low latency, and high capacity. Many enabling applications in 5G-VANET rely on sharing mobile data among vehicles, which is still a challenging issue due to the extremely large data volume and the prohibitive cost of transmitting such data using 5G cellular networks. This article focuses on efficient cooperative data sharing in edge computing assisted 5G-VANET. First, to enable efficient cooperation between cellular communication and Dedicated Short-Range Communication (DSRC), we first propose a software-defined cooperative data sharing architecture in 5G-VANET. The cellular link allows the communications between OpenFlow enabled vehicles and the Controller to collect contextual information, while the DSRC serves as the data plane, enabling cooperative data sharing among adjacent vehicles. Second, we propose a graph theory based algorithm to efficiently solve the data sharing problem, which is formulated as a maximum weighted independent set problem on the constructed conflict graph. Specifically, considering the continuous data sharing, we propose a balanced greedy algorithm, which can make the content distribution more balanced. Furthermore, due to the fixed amount of computing resources allocated to this software-defined cooperative data sharing service, we propose an integer linear programming based decomposition algorithm to make full use of the computing resources. Extensive simulations in NS3 and SUMO demonstrate the superiority and scalability of the proposed software-defined architecture and cooperative data sharing algorithms.

Efficiency and reliability are essential for isolated bidirectional dc–dc converters in electric vehicle (EV) battery charging systems. As one promising power interface between energy storage components and power bus, dual-active-bridge (DAB) dc–dc converters have gained wide applications due to the advantages of galvanic isolation, symmetric circuit structure, high power density, and easy control implementation. In this article, based on the triple-phase shift (TPS) control, a fast and effective fault diagnosis strategy is proposed for DAB converters to improve both the power conversion efficiency and reliability. First, the operation principle of DAB converters with TPS control is presented. Then, a fast and effective open-circuit fault (OCF) analysis under different operation modes of TPS control is presented. Based on the resonant phenomenon during the fault conditions, an OCF diagnosis strategy that uses midpoint voltage as diagnosis criteria is proposed for DAB converters under the advanced TPS control to identify the faulty devices quickly and avoid the system collapse. After the failure switches are located, fault-tolerant operation is adopted to maintain the power transmission ability of DAB converters and ensure safe operation. The experimental results are provided to validate the correctness and effectiveness of the OCF performance analysis, fault diagnosis, and tolerant method.

In China, due to labor shortages and increasing labor costs, manual topping is gradually being replaced by chemical capping with mepiquat chloride (DPC). External chemicals can adjust plant growth by affecting endogenous hormones. Based on the hormone changes combined with the development of the main stem of cotton plants, a comparative experiment was conducted in 2019 and 2020 to determine the regulatory mechanism of the growth of the cotton main stem after chemical capping. In the experiment, two treatment times (T1: 12 July, T2: 18 July) and two treatment agents (CA [chemical capping agent] and DPC) were set, the hormone (auxin IAA, abscisic acid ABA, Gibberellin GA3 and Zeatin ZR) concentrations at the top of main stem (0–5 and 5–10 cm) were continuously measured and the main stem development situation was observed and recorded. The results showed that after chemical capping, the IAA concentration decreased firstly and increased later, lower than that of CK. ABA concentration increased significantly and GA3 concentration decreased significantly compared with CK. ZR concentrations fluctuated obviously at T1 and gently at T2. In terms of main stem growth, the plant height, number of fruit branches and average length of upper internode (fifth and above) were decreased compared with CK, while the CA treatment was inhibited more strongly than the DPC treatment. To conclude, chemical capping operation affected the hormone concentration at the plant apex significantly and effectively regulated plant development. In comparison with DPC treatment, CA regulated hormones effectively, which is favorable for conducive reasonable plant shaping.

This paper introduces the sea-launch technology of a cryogenic liquid-fueled medium-lift rocket. It first reviews the current state of sea launch technology, and then gives a brief introduction of China’s New Generation Medium-lift Launch Vehicle (NGMLV). The innovations in the NGMVL, such as responsive test and launch control, a H3 launch model, and unmanned operations, provide convenience for sea launches. Based on these innovations, this paper proposes a sea launch scheme, including the system configuration, test and launch processes, and an improved adaptive design for the rocket. Then, the launch platform is discussed in detail, which integrates the functions of sea transportation, assembly and test, as well as technical and launch areas. The layout and function divisions, fluid filling, gas supply and distribution systems, and lossless storage technology of LH2 are described in order. This breakthrough in sea launch technology will enable China to launch medium and large satellites and constellations ‘both on land and sea’, especially into low-inclination Low-Earth Orbits (LEOs), and it allows China to remain competitive in the fast-paced space industry.

This letter theoretically formulates the coded cooperative data exchange (CCDE) problem in multi-channel multi-hop wireless networks, where a set of nodes initially hold a subset of packets and wish to retrieve all desired packets via multi-hop wireless communication. At first, we propose the model of tentative transmission, with which we can jointly consider resource allocation, transmission scheduling, and transmitted packet scheduling. Then, based on the time expanded graph, we convert the CCDE problem in multiple time slots into a single source and multiple sinks network (SSMN) within a time slot. Finally, the relationship between the required time slots and the number of channels is theoretically formulated based on the constructed SSMN. Experiments demonstrate that the gain due to the increasing number of channels flattens.

Zebrafish (Danio rerio) have attracted much attention over the past decade as a reliable model for gut microbiome research. Owing to their low cost, strong genetic and development coherence, efficient preparation of germ-free (GF) larvae, availability in high-throughput chemical screening, and fitness for intravital imaging in vivo, zebrafish have been extensively used to investigate microbiome-host interactions and evaluate the toxicity of environmental pollutants. In this review, the advantages and disadvantages of zebrafish for studying the role of the gut microbiome compared with warm-blooded animal models are first summarized. Then, the roles of zebrafish gut microbiome on host development, metabolic pathways, gut-brain axis, and immune disorders and responses are addressed. Furthermore, their applications for the toxicological assessment of aquatic environmental pollutants and exploration of the molecular mechanism of pathogen infections are reviewed. We highlight the great potential of the zebrafish model for developing probiotics for xenobiotic detoxification, resistance against bacterial infection, and disease prevention and cure. Overall, the zebrafish model promises a brighter future for gut microbiome research.

The modification of polymer with nanoparticles has been proved to have good results, but the key of this method is that nanoparticles can maintain uniform dispersion in the polymer. Herein, silica (SiO2 ) and zinc oxide (ZnO) hybrid nanofillers were prepared by self-made dispersion equipment to modify natural rubber latex (NRL). The results show that the uniformly dispersed nano-SiO2 fillers increase the number of entanglements in the molecular chain of NRL, thereby improving the tensile properties. The tensile strength and elongation at break of LZ1S4.2 were increased to 32.61 MPa and 957%, respectively. The increase of the number of entanglements also makes the structure of NRL more compact, and improves the barrier performance and aging resistance. The uniformly dispersed nano-ZnO fillers endow the NRL with excellent antibacterial properties, and the antibacterial rate (For Escherichia coli and Staphylococcus aureus ) is greater than 99.9%. In addition, NRL condoms and gloves incorporating hybrid nanofillers have been proved to have good biocompatibility, and this NRL nanocomposite is expected to be applied to other NRL products.

Pancreatic carcinoma is a highly fatal disease, and early diagnosis is of vital importance. This meta-analysis aimed to compare the diagnostic performances of contrast-enhanced ultrasonography (CEUS) against contrast-enhanced computed tomography (CECT) for pancreatic carcinoma, using pathological results or alternative imaging modality as the gold standard.A thorough search was conducted in PubMed, EMBASE, Web of Science and Cochrane Library databases. Two investigators selected the studies and extracted the data independently. A bivariate mixed-effects regression model was used to calculate the pooled data. Subgroup analysis and meta-regression were performed to explore the causes of heterogeneity.A total of 1,227 records were identified, of which 7 articles with 588 patients were assessed for eligibility. The overall sensitivity, specificity of CEUS and CECT with their 95% confidential intervals (95% CI) were 0.91 (0.85-0.94) and 0.88 (0.81-0.92), 0.83 (0.70-0.91) and 0.87 (0.73-0.94), respectively. The area under curve (AUC) of CEUS and CECT were 0.94 and 0.93. Subgroup analysis showed CEUS may be good at diagnosing lesions with diameters less than 2 cm. Tumor features, region and study type were the main causes of heterogeneity.CEUS has a satisfying diagnostic performance for pancreatic carcinoma and it has high sensitivity for small pancreatic carcinomas (≤2 cm); besides, it performs well in discriminating pancreatic cancer from chronic pancreatitis. Therefore, CEUS can be a useful supplement to CECT.