DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip - deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware

Bidirectional regulation of angiogenesis and miR-18a expression by PNS in the mouse model of tumor complicated by myocardial ischemia

BACKGROUND: Panax Notoginseng Saponins (PNS) is the major class of active constituents of notoginseng, a natural product extensively used as a therapeutic agent in China. Tumor when accompanied by cardiovascular disorders poses a greater challenge for clinical management given the paradoxical involvement of angiogenesis, therefore gaining increased research attention. This study aim to investigate effects of PNS and its activity components in the mouse model of tumor complicated with myocardial ischemia. METHODS: Tumor complexed with myocardial ischemia mouse model was first established, which was followed by histological and immunohistochemistry examination to assess the effect of indicated treatments on tumor, myocardial ischemia and tissue specific angiogenesis. MicroRNA (miRNA) profiling was further carried out to identify potential miRNA regulators that might mechanistically underline the therapeutic effects of PNS in this complex model. RESULTS: PNS and its major activity components Rg1, Rb1 and R1 suppressed tumor growth and simultaneously attenuated myocardial ischemia. PNS treatment led to decreased expression of CD34 and vWF in tumor and increased expression of these vascular markers in heart. PNS treatment resulted in reduced expression of miR-18a in tumor and upregulated expression of miR-18a in heart. CONCLUSIONS: Our data demonstrated for the first time that PNS exerts tissue specific regulatory effects on angiogenesis in part through modulating the expression of miR-18a, which could be responsible for its bidirectional effect on complex disease conditions where paradoxical angiogenesis is implicated. Therefore, our study provides experimental evidence warranting evaluation of PNS and related bioactive component as a rational therapy for complex disease conditions including co-manifestation of cancer and ischemic cardiovascular disease

Compounds purified from edible fungi fight against chronic inflammation through oxidative stress regulation

Chronic inflammation is associated with various chronic diseases, including cardiovascular disease, neurodegenerative disease, and cancer, which severely affect the health and quality of life of people. Oxidative stress induced by unbalanced production and elimination of reactive oxygen species (ROS) is one of the essential risk factors for chronic inflammation. Recent studies, including the studies of mushrooms, which have received considerable attention, report that the antioxidant effects of natural compounds have more advantages than synthetic antioxidants. Mushrooms have been consumed by humans as precious nourishment for 3,000 years, and so far, more than 350 types have been identified in China. Mushrooms are rich in polysaccharides, peptides, polyphenols, alkaloids, and terpenoids and are associated with several healthy biological functions, especially antioxidant properties. As such, the extracts purified from mushrooms could activate the expression of antioxidant enzymes through the Keap1/Nrf2/ARE pathway to neutralize excessive ROS and inhibit ROS-induced chronic inflammation through the NF-κB pathway. Recently, the antioxidant properties of mushrooms have been successfully applied to treating cardiovascular disease (CAD), neurodegenerative diseases, diabetes mellitus, and cancer. The present review summarizes the antioxidant properties and the mechanism of compounds purified from mushrooms, emphasizing the oxidative stress regulation of mushrooms to fight against chronic inflammation

Acoustic Three-dimensional Chern Insulators with Arbitrary Chern Vectors

The Chern vector is a vectorial generalization of the scalar Chern number, being able to characterize the topological phase of three-dimensional (3D) Chern insulators. Such a vectorial generalization extends the applicability of Chern-type bulk-boundary correspondence from one-dimensional (1D) edge states to two-dimensional (2D) surface states, whose unique features, such as forming nontrivial torus knots or links in the surface Brillouin zone, have been demonstrated recently in 3D photonic crystals. However, since it is still unclear how to achieve an arbitrary Chern vector, so far the surface-state torus knots or links can emerge, not on the surface of a single crystal as in other 3D topological phases, but only along an internal domain wall between two crystals with perpendicular Chern vectors. Here, we extend the 3D Chern insulator phase to acoustic crystals for sound waves, and propose a scheme to construct an arbitrary Chern vector that allows the emergence of surface-state torus knots or links on the surface of a single crystal. These results provide a complete picture of bulk-boundary correspondence for Chern vectors, and may find use in novel applications in topological acoustics

The hidden inequality: the disparities in the quality of daily use masks associated with family economic status

Wearing high-quality masks plays a critical role in reducing COVID-19 transmission. However, no study has investigated socioeconomic inequality in the quality of masks. Addressing this gap, this paper explored the relationships between mask’s quality and family economic status. The cross-sectional survey was conducted in two Chinese universities by distributing structured questionnaires to assess participants’ characteristics including family economic status, and meanwhile collecting their masks to evaluate the quality by measuring particle filtration efficiency. The valid responses were obtained from 912 students with mean age of 19.556 ± 1.453  years and were analyzed by using fractional or binary logistic regression. Three main findings were presented. First, inequality existed in the quality of masks. 36.07% of students were using unqualified masks with average filtration efficiency of 0.795 ± 0.119, which was much lower than China’s national standard (0.9). Of those masks with identified production date, 11.43% were manufactured during COVID-19 outbreak when market was flooded with counterfeit production, and thus were of poor quality with average filtration efficiency of 0.819 ± 0.152. Second, better family economic status was associated with better masks’ filtration efficiency and greater probability of using qualified masks. Third, students with better family economic status tend to use masks with individual packaging, and unique patterns and special designs, which may lead to inequality on a psychological level. Our analysis reveals the hidden socioeconomic inequality that exist behind cheap masks. In facing the challenges of future emerging infectious diseases, it is important to address the inequity to ensure equal access to affordable qualified personal protection equipment

The Mechanism of Two Benzaldehydes from Aspergillus terreus C23-3 Improve Neuroinflammatory and Neuronal Damage to Delay the Progression of Alzheimer’s Disease

Alzheimer’s disease (AD), a neurodegenerative disease, is the most common cause of dementia in humans worldwide. Although more in-depth research has been carried out on AD, the therapeutic effect of AD is not as expected, and natural active substances are increasingly sought after by scientists. In the present study, we evaluated two benzaldehydes from a coral-derived Aspergillus terreus strain C23-3, their anti-neuroinflammatory activity in microglia (BV-2), and their neuroprotective activity and mechanisms in hippocampal neuronal cells (HT-22). These include the protein expression of iNOS, COX-2, MAPKs pathways, Tau protein-related pathways, caspases family-related signaling pathways. They also include the levels of TNF-α, IL-6, IL-18 and ROS, as well as the level of mitochondrial oxidative stress and neuronal cell apoptosis. The results showed that both benzaldehydes were effective in reducing the secretion of various inflammatory mediators, as well as pro-inflammatory factors. Among these, benzaldehyde 2 inhibited mitochondrial oxidative stress and blocked neuronal cell apoptosis through Tau protein-related pathways and caspases family-related signaling pathways, thereby inhibiting β-amyloid (Aβ)-induced neurological damage. This study reveals that benzaldehyde 2 has potential as a therapeutic agent for Alzheimer’s disease, and offers a new approach to the high-value use of marine natural products

Anomaly Detection of Permanent Magnet Synchronous Motor Based on Improved DWT-CNN Multi-Current Fusion

The Permanent Magnet Synchronous Motor (PMSM) is the power source maintaining the stable and efficient operation of various pieces of equipment; hence, its reliability is crucial to the safety of public equipment. Convolutional Neural Network (CNN) models face challenges in extracting features from PMSM current data. A new Discrete Wavelet Transform Convolutional Neural Networks (DW-CNN) feature with fusion weight updating Long Short-Term Memory (LSTM) anomaly detection is proposed in this paper. This approach combines Discrete Wavelet Transform (DWT) with high and low-frequency separation processing and LSTM. The anomaly detection method adopts DWT and CNN by separating high and low-frequency processing. Moreover, this method combines the hybrid attention mechanism to extract the multi-current signal features and detects anomalies based on weight updating the LSTM network. Experiments on the motor bearing real fault dataset and the PMSM stator fault dataset prove the method’s strong capability in fusing current features and detecting anomalies

Optimization and GIS-based combined approach for the determination of sites and size of biogas plants for a whole region

To make better use of agricultural residues and solve the problem of residues pollution, it is necessary to carry out regional management, which means spatial planning of the entire region is essential. This study developed a methodology based on GIS for determining the suitable locations, optimal sizes and number of biogas plants for the entire region while meeting the conditions that all biomass can be collected. Based on the optimization of transportation distance, the nearest facility model and the modified location allocation model were used to obtain the correspondence between plants and supply points, the transportation path and the plants’ capacity under different numbers of plants. Based on economic optimization, the economic model was constructed to calculate the total cost of different numbers of biogas plants and the optimal number was obtained after comparing. The cross path was adjusted for the selected plan to ensure that there was no crossover in the plants’ collection area. This approach was applied (as a case study) in Funan County, Anhui Province. Based on the existing results, the optimal construction number of biogas plants in the region was 9