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Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. The results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.

We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control problems both in experiments and simulations by automatically discovering active control strategies for drag reduction in turbulent flow. Specifically, we aimed to maximize the power gain efficiency by properly selecting the rotational speed of two small cylinders, located parallel to and downstream of the main cylinder. By properly defining rewards and designing noise reduction techniques, and after an automatic sequence of tens of towing experiments, the RL agent was shown to discover a control strategy that is comparable to the optimal strategy found through lengthy systematically planned control experiments. Subsequently, these results were verified by simulations that enabled us to gain insight into the physical mechanisms of the drag reduction process. While RL has been used effectively previously in idealized computer flow simulation studies, this study demonstrates its effectiveness in experimental fluid mechanics and verifies it by simulations, potentially paving the way for efficient exploration of additional active flow control strategies in other complex fluid mechanics applications.

Vortex-induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible Navier–Stokes equations coupled with the structure’s dynamic motion equation. In the first case, given scattered data in space–time on the velocity field and the structure’s motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure’s dynamic motion. In the second case, given scattered data in space–time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems enables velocity and pressure quantification from flow snapshots in small subdomains and can be exploited for flow control applications and also for system identification.

Outstanding overall mechanical properties are essential for the successful utilization of hydrogels in advanced applications such as human-machine interfaces and soft robotics. However, conventional hydrogels suffer from fracture toughness-stiffness conflict and fatigue threshold-stiffness conflict, limiting their applicability. Simultaneously enhancing the fracture toughness, fatigue threshold, and stiffness of hydrogels, especially within a homogeneous single network structure, has proven to be a formidable challenge. In this work, we overcome this challenge through the design of a loosely cross-linked hydrogel with slight dehydration. Experimental results reveal that the slightly-dehydrated, loosely cross-linked polyacrylamide hydrogel, with an original/current water content of 87%/70%, exhibits improved mechanical properties, which is primarily attributed to the synergy between the long-chain structure and the dense dehydration-induced entanglements. Importantly, the creation of these microstructures does not require intricate design or processing. This simple approach holds significant potential for hydrogel applications where excellent anti-fracture and fatigue-resistant properties are necessary. For use in a range of applications, hydrogel properties are vital, but it can be challenging to balance different aspects. Here, the authors report the use of a dehydration strategy to prepare hydrogels with desirable fracture toughness, fatigue threshold, and modulus.

The eco-environmental problems have become a hot topic of global concern today. Many scholars seek intellectual resources from traditional cultures to solve these problems. This article tries to extract the rich ecological thoughts from the text of Laozi. According to Laozi, Tao is ‘the mother of all beneath Heaven’, that is, the whole universe, including man and nature, evolve from Tao. This indicates the primary harmony of man and nature. However, man mistakenly regards himself as the lord of nature, and uses his force and methods of science and technology to enslave nature, causing the current ecological crisis. The way to resolve the crisis is pointed out in the following statement: ‘Man should imitate Earth, Earth should imitate Heaven, Heaven should imitate Tao, and Tao is being what it is’. Laozi advocates man to imitate nature, leading a life of ‘having no self’, ‘wu wei’ or ‘desiring no-desire’. This is the Tao-centred way of living, which brings about a transformation in human existence.ContributionThis article argues that the current ecological crisis is caused by mankind’s mistaken belief that they are the masters of nature. The philosophical ideas of Laozi provide a way to resolve the crisis, advocating that humans should imitate nature and adopt a Tao-centred way of living. This way of living brings about a transformation in human existence. The article provides a philosophical perspective on how to solve contemporary environmental problems.

Due to their outstanding overall performances, carbon fiber/poly(ether-ether-ketone) (CF/PEEK) composites have attracted a lot of interest recently. High-performance CF/PEEK composites have many advantages such as high strength, good toughness, and high service temperature, which have been widely used in various high-precision fields. This paper reviews the research progress of the CF/PEEK composite molding process and its applications in view of the research hotspots in recent years, laying the foundation for the research on the preparation technology and industrial application of the material. CF/PEEK molding process mainly includes injection molding, press molding, filament winding, 3D Printing, and automated fiber placement (AFP). Different processing methods and material ratios result in CF/PEEK materials with different mechanical properties. Since CF/PEEK has its own unique advantages over traditional metal materials in terms of mechanical properties, corrosion resistance, and density, CF/PEEK materials can be used to replace metal materials in many applications, such as aviation and aerospace, biomedical field and automotive part.

Women worldwide are more likely to develop breast cancer (BC) than any other type of cancer. The treatment of BC depends on the subtype and stage of the cancer, such as surgery, radiotherapy, chemotherapy, and immunotherapy. Although significant progress has been made in recent years, advanced or metastatic BC presents a poor prognosis, due to drug resistance and recurrences. During embryonic development, myeloid-derived suppressor cells (MDSCs) develop that suppress the immune system. By inhibiting anti-immune effects and promoting non-immune mechanisms such as tumor cell stemness, epithelial-mesenchymal transformation (EMT) and angiogenesis, MDSCs effectively promote tumor growth and metastasis. In various BC models, peripheral tissues, and tumor microenvironments (TME), MDSCs have been found to amplification. Clinical progression or poor prognosis are strongly associated with increased MDSCs. In this review, we describe the activation, recruitment, and differentiation of MDSCs production in BC, the involvement of MDSCs in BC progression, and the clinical characteristics of MDSCs as a potential BC therapy target.

Background The implementation of evidence-based guidelines can improve clinical and public health outcomes by helping health professionals practice in the most effective manner, as well as assisting policy-makers in designing optimal programs. Adaptation of a guideline to suit the context in which it is intended to be applied can be a key step in the implementation process. Without taking the local context into account, certain interventions recommended in evidence-based guidelines may be infeasible under local conditions. Guideline adaptation frameworks provide a systematic way of approaching adaptation, and their use may increase transparency, methodological rigor, and the quality of the adapted guideline. This paper presents a number of adaptation frameworks that are currently available. We aim to compare the advantages and limitations of their processes, methods, and resource implications. These insights into adaptation frameworks can inform the future development of guidelines and systematic methods to optimize their adaptation. Analysis Recent adaptation frameworks show an evolution from adapting entire existing guidelines, to adapting specific recommendations extracted from an existing guideline, to constructing evidence tables for each recommendation that needs to be adapted. This is a move towards more recommendation-focused, context-specific processes and considerations. There are still many gaps in knowledge about guideline adaptation. Most of the frameworks reviewed lack any evaluation of the adaptation process and outcomes, including user satisfaction and resources expended. The validity, usability, and health impact of guidelines developed via an adaptation process have not been studied. Lastly, adaptation frameworks have not been evaluated for use in low-income countries. Conclusion Despite the limitations in frameworks, a more systematic approach to adaptation based on a framework is valuable, as it helps to ensure that the recommendations stay true to the evidence while taking local needs into account. The utilization of frameworks in the guideline implementation process can be optimized by increasing the understanding and upfront estimation of resource and time needed, capacity building in adaptation methods, and increasing the adaptability of the source recommendation document.

Background: Doxorubicin (DOX), a broad-spectrum chemotherapy drug, is clinically employed to treat cancers especially for breast cancer and lung cancer. But its clinical applications are limited by the dose-dependent cardiac toxicity. Resveratrol (Res), a polyphenolic antitoxin, has been proved to be capable of improving the cardiomyocyte calcium cycling by up-regulating SIRT-1-mediated deacetylation to inhibit DOX-induced cardiotoxicity. Purpose: The objective of this study was to develop a solid lipid nanoparticle (SLN) loaded with Res to trigger inhibition of DOX-induced cardiotoxicity. Methods: Res-SLN was prepared by emulsification-diffusion method followed by sonication and optimized using central composite design/response surface method. The Res-SLN was further evaluated by dynamic light scattering, transmission electron microscopy for morphology and high performance liquid chromatography for drug loading and release profile. And the Res distribution in vivo was determined on rats while the effect of inhibit DOX-induced cardiotoxicity was investigated on mice. Results: Res-SLN with homogeneous particle size of 271.13 nm was successfully formulated and optimized. The prepared Res-SLN showed stable under storage and sustained release profile, improving the poor solubility of Res. Heart rate, ejection fractions and fractional shortening of Res-SLN treating mice were found higher than those on mice with cardiac toxicity induced by single high-dose intraperitoneal injection of DOX. And the degree of myocardial ultrastructural lesions on mice was also observed. Conclusion: Res-SLN has a certain therapeutic effect for protecting the myocardium and reducing DOX-induced cardiotoxicity in mice. Keywords: resveratrol, solid lipid nanoparticles, doxorubicin, heart failure

The mortality rate of liver cancer is gradually increasing worldwide due to the increasing risk factors such as fatty liver, diabetes, and alcoholic cirrhosis. The diagnostic methods of liver cancer include ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), among others. The treatment of liver cancer includes surgical resection, transplantation, ablation, and chemoembolization; however, treatment still faces multiple challenges due to its insidious development, high rate of recurrence after surgical resection, and high failure rate of transplantation. The emergence of liposomes has provided new insights into the treatment of liver cancer. Due to their excellent carrier properties and maneuverability, liposomes can be used to perform a variety of functions such as aiding in imaging diagnoses, combinatorial therapies, and integrating disease diagnosis and treatment. In this paper, we further discuss such advantages.