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In underground engineering with complex conditions, the bolt (cable) anchorage support system is in an environment where static and dynamic stresses coexist, under the action of geological conditions such as high stresses and strong disturbances and construction conditions such as the application of high prestress. It is essential to study the support components performance under dynamic-static coupling conditions. Based on this, a multi-functional anchorage support dynamic-static coupling performance test system (MAC system) is developed, which can achieve 7 types of testing functions, including single component performance, anchored net performance, anchored rock performance and so on. The bolt and cable mechanical tests are conducted by MAC system under different prestress levels. The results showed that compared to the non-prestress condition, the impact resistance performance of prestressed bolts (cables) is significantly reduced. In the prestress range of 50–160 kN, the maximum reduction rate of impact energy resisted by different types of bolts is 53.9%–61.5% compared to non-prestress condition. In the prestress range of 150–300 kN, the impact energy resisted by high-strength cable is reduced by 76.8%–84.6% compared to non-prestress condition. The MAC system achieves dynamic-static coupling performance test, which provide an effective means for the design of anchorage support system.

In order to study the damage and failure of asphalt mixture under uniaxial loading under freeze-thaw cycle, a damage constitutive model is constructed based on the Particle Flow Code (PFC2D). Through four sets of uniaxial compression laboratory tests with different freeze-thaw cycles, numerical simulation with the help of PFC2D, analysis of macroscopic failure of asphalt mixture and evolution law of internal microcracks from mesoscopic perspective, the equation of the relationship between the damage variable and the strain is established. The results of the study showed that: (1) The numerical simulations are in good agreement with the experimental results. (2) The increase of freeze-thaw cycles will increase the freeze-thaw damage variable and peak strain of asphalt mixture and reduce the compressive strength of asphalt mixture. (3) The established piecewise function expression can fit the relationship between the damage variable and the strain well. This study provides a convenient method for the mesoscopic study of asphalt mixture loading test under freeze-thaw cycle.

The advancement of artificial intelligence (AI) and the ubiquity of social media have become transformative agents in contemporary educational ecosystems. The spotlight of this inquiry focuses on the nexus between AI and social media usage in relation to academic performance and mental well-being, and the role of smart learning in facilitating these relationships. Using partial least squares–structural equation modeling (PLS-SEM) on a sample of 401 Chinese university students. The study results reveal that both AI and social media have a positive impact on academic performance and mental well-being among university students. Furthermore, smart learning serves as a positive mediating variable, amplifying the beneficial effects of AI and social media on both academic performance and mental well-being. These revelations contribute to the discourse on technology-enhanced education, showing that embracing AI and social media can have a positive impact on student performance and well-being.

Currently, important privacy data of the Internet of Things (IoT) face extremely high risks of leakage. Attackers persistently engage in continuous attacks on terminal devices to obtain private data of crucial importance. Although significant progress has been made in recent years in deep reinforcement learning defense strategies, most defense methods still face problems such as low defense resource allocation efficiency and insufficient defense coordination capabilities. To solve the above problems, this paper constructs a novel adversarial security scenario and proposes a security game model that integrates defense resource allocation and patrol inspection. Regarding the above game model, this paper designs a deep reinforcement learning algorithm named SDSA to calculate its security defense strategy. SDSA calculates the allocation strategy of the best patrolling strategy that is most suitable for the defender by searching the policy on a multi-dimensional discrete action space, and enables multiple defense agents to cooperate efficiently by training a multi-intelligent Dueling Double Deep Q-Network (D3QN) with prioritized experience replay. Finally, the experimental results show that the SDSA-learned security defense strategy can provide a feasible and effective security protection strategy for defenders against attacks compared to the MADDPG and OptGradFP methods.

Wood is renewable and biodegradable natural resource which has been widely used in construction industry and home furnishing; however, it absorbs moisture easily limiting its application. Herein, an ultra-hydrophobic wood was developed with acetylated β-cyclodextrin (Ac-β-CD). Due to the unique hydrophobic cavity structure of Ac-β-CD, a special micro/nanostructure was constructed on the wood cell wall achieving a maximum water contact angle of 150.5°. Also, it was stable at 137°, with a contact angle change rate of 1.16% during 10–100 s. The tensile strength of Ac-β-CD-wood increased by 58.76% due to the hydrogen bond network across the wood, and it also showed good performance in oil absorption capacity. Moreover, Ac-β-CD-wood can withstand more than 100 wipes while retaining its hydrophobic property. Therefore, this study provides a new eco-friendly strategy for producing biodegradable hydrophobic oil-absorbing wood with outstanding tensile strength and dimensional stability.

Ferroptosis is a recently identified form of regulated cell death that plays a crucial role in tumor suppression. In this study, we found that F2 (the gene encoding thrombin) was strongly upregulated in breast cancer (BRCA, TCGA Study Abbreviations) compared with normal samples and that lower F2 levels were associated with poorer prognosis in breast cancer patients. Thrombin induces ferroptosis in triple-negative breast cancer (TNBC) cells by activation of cytosolic phospholipase A2α (cPLA2α) activity to increase the release of arachidonic acid (AA). TNBC in all breast cancer subtypes exhibited the highest levels of PLA2G4A (the gene encoding cPLA2α) and Acsl4, and inhibition of cPLA2α and its downstream enzyme acyl-CoA synthetase long-chain family member 4 (ACSL4) reversed thrombin toxicity. In a mouse xenograft model of TNBC, thrombin treatment suppressed breast cancer growth which can be inhibited by ferroptosis inhibitor Liproxstatin-1 (Lip-1). Our study underscores the potential of the thrombin-ACSL4 axis as a promising therapeutic target for the treatment of TNBC.

Introduction: Considerable evidence has linked periodontitis (PD) to hypertension (HTN), but the nature behind this connection is unclear. Dysbiosis of oral microbiota leading to PD is known to aggravate different systematic diseases, but the alteration of oral microbiota in HTN and their impacts on blood pressure (BP) remains to be discovered. Objectives: To characterize the alterations of oral and gut microbiota and their roles in HTN. Methods: We performed a cross-sectional (95 HTN participants and 39 controls) and a 6-month follow-up study (52 HTN participants and 26 controls) to analyze the roles of oral and gut microbiota in HTN. Saliva, subgingival plaques, and feces were collected for 16S rRNA gene sequencing or metagenomic analysis. C57BL/6J mice were pretreated with antibiotics to deplete gut microbiota, and then transplanted with human saliva by gavage to test the impacts of abnormal oral-gut microbial transmission on HTN. Results: BP in participants with PD was higher than no PD in both cross-sectional and follow-up cohort. Relative abundances of 14 salivary genera, 15 subgingival genera and 10 gut genera significantly altered in HTN and those of 7 salivary genera, 12 subgingival genera and 6 gut genera significantly correlated with BP. Sixteen species under 5 genera were identified as oral-gut transmitters, illustrating the presence of oral-gut microbial transmission in HTN. Veillonella was a frequent oral-gut transmitter stably enriched in HTN participants of both cross-sectional and follow-up cohorts. Saliva from HTN participants increased BP in hypertensive mice. Human saliva-derived Veillonella successfully colonized in mouse gut, more abundantly under HTN condition. Conclusions: PD and oral microbiota are strongly associated with HTN, likely through oral-gut transmission of microbes. Ectopic colonization of saliva-derived Veillonella in the gut may aggravate HTN. Therefore, precise manipulations of oral microbiota and/or oral-gut microbial transmission may be useful strategies for better prevention and treatment of HTN.

Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.

This research study aims to reveal the role of supply chain parameters approved by blockchain technology toward firm performance through trust. This study has also examined the moderating role of government support between trust and firm performance. The underlying theories reinforce the usefulness and flexibility of a supply chain in regulating daily fluctuations and uncertainties in supply and demand. Blockchain technology adoption with supply chain tactics provides a more suitable environment for better firm performance. Governments address legal and security concerns related to blockchain technology and provide clear guidelines and standards for its use in supply chains, which build trust among firms and stakeholders. A conceptual model has been developed with the assistance of past empirical research studies and associated theories. This research study has examined the following relationships from a sample of 465 employees from textile industrial firms listed on the Pakistan Stock Exchange. The current research study assesses these parameters using the partial least squares structural equation modeling (PLS-SEM) method. The analysis showed that supply chain parameters (alignment, agility, adaptability) approved by blockchain technology positively correlate with firm performance. Trust positively mediated the relationship between supply chain parameters approved by blockchain technology and firm performance. Furthermore, government support positively moderated the relationship between trust and firm performance. The study would provide directions for further research. These findings will give the global supply chain industry valuable insights into blockchain technology for firm performance. In theory, this research study would contribute to the scientific literature by answering how trust and government support affect the overall firm performance.

Near-surface temperatures, such as air, land surface, and soil temperatures, play significant roles in surface radiation and energy balance. This study assessed nine gridded near-surface temperature products and analyzed the spatial heterogeneity and clear-sky bias of these temperature variables, using extensive measurements collected at Heihe River Basin. The MXD21 (MOD21 and MYD21) product had the lowest root mean square error (RMSE) (3.35 K) among all skin temperature products but a high percentage of missing values (48.4 %). All-weather skin temperature products had comparable accuracy for the interpolated cloudy-sky cases (RMSE 4.92 K) and observed clear-sky pixels (RMSE 3.42 K). For air temperature, AMSR2 had the lowest RMSE (2.48 K), but a high percentage of invalid data (32.5 %); and ERA5 had a worse accuracy (RMSE 3.87 K) but a high spatial resolution and gap-free data coverage. Comparing products from the same data source, air and soil temperatures had higher accuracies than skin temperature. Among the different variables of temperature, the 0 cm soil temperature and skin temperature had higher spatiotemporal heterogeneity than the air temperature and the soil temperatures at greater depths. The skin temperature, 0 cm soil temperature, and air temperature had higher clear-sky biases compared to soil temperatures.

The end Ordovician mass extinction (EOME) was the second most severe biotic crisis in Phanerozoic, and has been widely linked to a major glaciation. However, robust geochronology of this interval is still lacking. Here we present four successive high-precision zircon U–Pb dates by isotope dilution thermal ionization mass spectrometry (ID-TIMS) for biostratigraphically well-constrained K-bentonites of a continuous Ordovician-Silurian boundary section at Wanhe, SW China. They include 444.65 ± 0.22 Ma (middle Dicellograptus complexus Biozone), 444.06 ± 0.20 Ma (lower Paraorthograptus pacificus Biozone), 443.81 ± 0.24 Ma (upper Tangyagraptus typicus Subzone), and 442.99 ± 0.17 Ma (upper Metabolograptus extraordinarius Biozone). Calculations based on sedimentation rates suggest a duration of 0.47 ± 0.34 Ma for the Hirnantian Stage, which is much shorter than previously thought (1.4 ± 2.05 Ma in the International Chronostratigraphic Chart ver. 2019/05). The new data also constrain the Hirnantian glacial maximum to ∼0.2 Ma, supporting that its brevity and intensity probably triggered the EOME. Keywords: ID-TIMS, K-bentonite, End Ordovician mass extinction (EOME), Hirnantian stage

Terrain-aided navigation (TAN) holds high potential for long-term accurate navigation of autonomous underwater vehicles (AUVs), and path planning algorithms are essential in TAN to decrease positioning errors by avoiding flat areas. This study proposed an AUV localization and path planning algorithm for TAN, which consists of a value function calculation and online path planning. In the value function calculation, the topographic complexity is treated as a factor that influences AUV state transition probabilities to calculate the optimal policy; meanwhile, the online path planning applies a particle filter to localize and command AUVs, and particle weights are calculated according to topographic complexity. Simulation experimental results demonstrate that this algorithm could provide paths with accurate TAN location results and good maneuvering performance., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)