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Clustered tensegrity structures integrated with continuous cables are lightweight, foldable, and deployable. Thus, they can be used as flexible manipulators or soft robots. The actuation process of such soft structure has high probabilistic sensitivity. It is essential to quantify the uncertainty of actuated responses of the tensegrity structures and to modulate their deformation accurately. In this work, we propose a comprehensive data-driven computational approach to study the uncertainty quantification (UQ) and probability propagation in clustered tensegrity structures, and we have developed a surrogate optimization model to control the flexible structure deformation. An example of clustered tensegrity beam subjected to a clustered actuation is presented to demonstrate the validity of the approach and its potential application. The three main novelties of the data-driven framework are: (1) The proposed model can avoid the difficulty of convergence in nonlinear Finite Element Analysis (FEA), by two machine learning methods, the Gauss Process Regression (GPR) and Neutral Network (NN). (2) A fast real-time prediction on uncertainty propagation can be achieved by the surrogate model, and (3) Optimization of the actuated deformation comes true by using both Sequence Quadratic Programming (SQP) and Bayesian optimization methods. The results have shown that the proposed data-driven computational approach is powerful and can be extended to other UQ models or alternative optimization objectives.

Traditional villages are an important cultural heritage left by China's agrarian civilization and serve as a testament to the historical development of the Chinese nation. The study of spatial and cultural differentiation in traditional villages is significant for their future preservation and development. Existing studies predominantly adopt a macro perspective, focusing on large-scale regions, and lack investigations from a micro perspective in medium- and small-scale areas. This study utilizes ArcGIS 10.8 for spatial analysis, multi-factor geographic detectors, and cultural geography spatial zoning methods to explore the spatial structure and cultural differentiation of 305 traditional villages in western Henan. The results indicate that the distribution of traditional villages in this region is significantly clustered and uneven, primarily concentrated in specific districts of Sanmenxia and Luoyang. Per capita GDP and the distance to roads are negatively correlated with the distribution of traditional settlements, reflecting the positive impact of lower economic levels and remote locations on village preservation. The spatial layout of traditional villages in western Henan exhibits clustering patterns, with cultural zoning characterized by distinct residential forms. This study, through the analysis of the spatial structure characteristics and influencing factors of traditional villages in the western Henan region, provides a new perspective on the formation and evolution of traditional villages, revealing the cultural differentiation characteristics of western Henan. The research results offer directional guidance for the conservation strategies of traditional villages in western Henan and provide a decision-making reference for cultural heritage conservation practices in similar regions. [ABSTRACT FROM AUTHOR]

Urban vitality is a concept that reflects dynamic changes in economic, social, and cultural aspects, emphasizing the importance of diversified land use and dense population. With technological advancements, research methods on urban vitality are increasingly diverse, particularly with the application of big data and geographic information systems providing new perspectives and tools for such studies. Currently, research on the vitality of inland Central Plains cities in China is relatively limited and largely confined to specific administrative areas, leading to an inadequate understanding of basic economic activities and population distribution within cities. Therefore, this study aims to explore the spatial distribution characteristics of urban vitality and its influencing factors in Zhengzhou's main urban area, providing a scientific basis for urban planning and sustainable development. This study utilizes methods that include Densi graph curve analysis, the entropy method, and the multiscale geographically weighted regression (MGWR) model, integrating statistical data, geographic information, and remote sensing imagery of Zhengzhou in 2023. The MGWR model analysis reveals: (1) Urban vitality in Zhengzhou's main urban area exhibits a concentric pattern, with high vitality at the center gradually decreasing toward the periphery, showing significant spatial differences in economic, population, and cultural vitality. (2) Various influencing factors positively correlate with urban vitality in the main urban area, but due to shortcomings in urban development strategies and planning, some factors negatively impact vitality in the central area while positively affecting vitality in peripheral areas. Based on these findings, this study provides relevant evidence and theoretical support for urban planning and sustainable development in Zhengzhou, aiding in the formulation of more effective urban development strategies. [ABSTRACT FROM AUTHOR]

As one form of urban green infrastructure, city parks provide essential ecosystem services and serve as a vital link connecting citizens with natural ecosystems. The public, as the primary beneficiaries of urban park ecosystem services, plays a crucial role in guiding the improvement and development trends of urban park planning. Most existing studies assess the comprehensive value of green spaces from a macro scale, with few focusing on the cultural and regulatory ecosystem services of urban parks and public perception of these services at the park level. This study targets 11 typical urban parks in Luoyang City, collecting 771 samples through field mapping and surveys. It analyzes users' socio-demographic characteristics, perception abilities, usage preferences, and service satisfaction, linking these subjective factors with objective elements of the park environment, such as fitness and recreational facilities. The results indicate that respondents with knowledge of ecosystem services are better able to evaluate and understand services such as air pollution reduction and noise reduction in parks, while those without such knowledge tend to focus more on services that they can intuitively perceive, such as biodiversity conservation and microclimate regulation. Additionally, the middle-income group exhibits a higher level of awareness of park ecosystem services, which depends on their active social participation. The findings of this study suggest that enhancing public understanding of ecosystem services through environmental education is key to improving the quality of urban ecosystems. [ABSTRACT FROM AUTHOR]

A technique called arch-clamping was used at our institute to ensure perfusion of the lower body and brain during total arch replacement with frozen elephant trunk (TAR and FET). The aortic arch clamp is inserted between the left common carotid artery and the left subclavian artery after inserting the stented elephant trunk into the true lumen of the descending aorta during the procedure, and then clamps the aorta and graft together as the distal anastomotic edge of the aorta. After the arch clamp was in place, lower body perfusion was resumed through the femoral artery was resumed and time to circulatory arrest was reduced to approximately 4 min. Cardiopulmonary bypass (CPB) flow was gradually restored to full rate. Thereafter, the left carotid artery anastomosis was completed and rewarming began. Finally, during the rewarming period, other branches of the aortic arch and ascending aorta were reconstructed. In this paper, we describe the perfusion management strategy, discuss intraoperative monitoring parameters, and examine the feasibility of the technique from a perfusion perspective. [ABSTRACT FROM AUTHOR]

In this work, a deep neural network is presented which is trained on flamelet/progress variable (FPV) tables and validated in a combustion large eddy simulation (LES) of the Sydney/Sandia flame with inhomogeneous inlets. Using data scaling and transformation techniques, as well as novel network architectures as the residual skip connection we are able to store all combustion relevant quantities in one single network, thus reducing effectively the memory footprint compared to the FPV tables, while keeping data retrieval times similar to table interpolation. The accuracy of the deep neural networks (DNN) is compared to its FPV counterpart and achieves excellent agreement. The DNN is also able to accurately predict the stiff progress variable source term ω ˙ ˜ P V in the thin reaction zone. Finally, the DNN thermochemistry representation is validated in combustion simulations of a 2D laminar premixed flame and a 3D LES of the Sydney/Sandia piloted jet flame with inhomogeneous inlet. The DNN results are in very good agreement with the conventional tabulated FPV simulations and show a promising way to efficiently reduce the storage size of high dimensional pretabulated thermochemical state space in reactive flow simulations. Abbreviations: ANN: Artificial neural network; API: Application programming interface; CFD: Computational fluid dynamics; CFL: Courant-Friedrichs-Lewy; CPU: Central processing unit; DL: Deep learning; DNN: Deep neural network; FGM: Flamelet generated manifold; FLOPS: Floating point operation per second; FPV: Flamelet/progress variable; GPU: Graphics processing unit; HPC: High-performance computing; ILDM: Intrinsic low-dimensional manifold; LES: Large eddy simulation; MAE: Mean absolute error; MPI: Message passing interface; MSE: Mean squared error; PDF: Probability density function; SGS: Subgrid scale; SOM: Self-organizing map; TCI: Turbulence–chemistry interaction. [ABSTRACT FROM AUTHOR]

A novel hybrid modeling approach that combines a finite-volume large eddy simulation (LES) with a Reynold-Averaged Navier-Stokes (RANS) conditional probability density function (PDF) method for turbulent non-premixed reacting flows is presented. The hybrid method incorporates the turbulent flow modeling of the LES with the chemistry–turbulence interaction modeling of the Lagrangian RANS-PDF. Specifically, the conditional composition RANS-PDF method is applied as the turbulence-chemistry closure for the LES. For a statistically steady-state case, the evolution of stochastic particles in a RANS framework reduces the computational effort, compared to the classical LES filtered density function (FDF), by performing the expensive chemical ODE integration at a lower spatial resolution. This approach is justified by the slow spatial variations of the reacting variables conditioned on the mixture fraction. Here, a consistent two-way coupling between the LES flow-field and the conditional composition RANS-PDF is formulated. The accuracy and the consistency of the method are established studying the Sandia flame series with detailed chemistry. Good predictions of the mean flow-fields and mean and conditional reactive scalars have been obtained, compared with the experimental data, with an overall decrease of the computational effort up to two orders of magnitude. [ABSTRACT FROM AUTHOR]

The computational demanding LES methods have widely demonstrated their reliability in the description of large scale unsteady phenomena in turbulent reactive flows. RANS Transported Probability Density Function (TPDF) methods treat the nonlinear chemical reactions in closed form on relatively coarse grids and using a smaller number of stochastic particles. Combining the two approaches, a hybrid LES-RANS PDF method to predict non-premixed turbulent flames is presented. In this method a LES, based on Smagorinsky's model and steady flamelet, is performed; subsequently, the calculated flow-field is used to drive the RANS-TPDF equation, which is closed at the joint scalar level and based on a Lagrangian Monte Carlo scheme. The required velocity and turbulent quantities for RANS simulation are estimated from the resolved LES and an algebraic model based on dimensional analysis and the mixing length hypothesis. The results of the velocity, turbulent kinetic energy and mixture fraction show that the consistency of the method is achieved. [ABSTRACT FROM AUTHOR]

Aortic dissection (AD), caused by tearing of the intima and avulsion of the aortic media, is a severe threat to patient life and organ function. Iron is closely related to dissection formation and organ injury, but the mechanism of iron ion transport disorder in endothelial cells (ECs) remains unclear. We identified the characteristic EC of dissection with iron overload by single-cell RNA sequencing data. After intersecting iron homeostasis and differentially expressed genes, it was found that hypoxia-inducible factor-1α (HIF-1α) and divalent metal transporter 1 (DMT1) are key genes for iron ion disorder. Subsequently, IL-6R was identified as an essential reason for the JAK-STAT activation, a classical iron regulation pathway, through further intersection and validation. In in vivo and in vitro , both high IL-6 receptor expression and elevated IL-6 levels promote JAK1-STAT3 phosphorylation, leading to increased HIF-1α protein levels. Elevated HIF-1α binds explicitly to the 5'-UTR sequence of the DMT1 gene and transcriptionally promotes DMT1 expression, thereby increasing Fe 2+ accumulation and endoplasmic reticulum stress (ERS). Blocking IL-6R and free iron with deferoxamine and tocilizumab significantly prolonged survival and reduced aortic and organ damage in dissection mice. A comparison of perioperative data between AD patients and others revealed that high free iron, IL-6, and ERS levels are characteristics of AD patients and are correlated with prognosis. In conclusion, activated IL-6/JAK1/STAT3 signaling axis up-regulates DMT1 expression by increasing HIF-1α, thereby increasing intracellular Fe 2+ accumulation and tissue injury, which suggests a potential therapeutic target for AD., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Objective: To evaluate the effect of aorta-iliac bypass total thoracoabdominal aorta aneurysm repair to spinal cord function., Methods: This was a prospective study. From June 2014 to April 2015, 31 patients underwent total thoracoabdominal aorta aneurysm repair were treated with aorta-iliac bypass technique. There were 23 male and 8 female patients with a mean age of (36±12) years. A 4-branched tetrafurcate graft was used. The aorta-iliac bypass was established, then distal descending aorta was perfused in a retrograde fashion via bypass graft. Thoracic and abdominal aorta were replaced in a staged fashion. Evoked potentials (EP) monitoring was adopted to assess the spinal cord ischemia throughout the procedure. The intraoperative evoked potentials results, clinical outcomes and follow-up results of this technique were evaluated., Results: The EP wave disappeared after proximal descending aorta clamped and gradually recovered after the patent segmental arteries reattached. Motor evoked potentials disappeared for (56±18) minutes, somatosensory evoked potentials disappeared for (50±19) minutes. The EP wave was restored to normal at the end of operation in all cases. The somatosensory evoked potentials remained unchanged in 2 cases (false negative). One case died after operation. There were acute kidney dysfunction in 3 cases, and pulmonary haemorrhage in 1 case. No spinal cord injure occurred. The median follow-up after operation was 8 months (ranging from 1 to 11 months). There was no delayed neurologic deficit or relative death., Conclusions: There is a transient function loss of spinal cord during the aorta-iliac bypass total thoracoabdominal aorta aneurysm repair. But the process is reversible. The technique of the aorta-iliac bypass is practicable.