Investigation of flow field characteristics and performance of carbon-hydrogen/oxygen-rich air rotating detonation engine

Numerical simulations were conducted to investigate the flow field characteristics and performance of a carbon-hydrogen/oxygen-rich air rotating detonation engine (RDE). Three distinct flow field structures were observed in the gas-solid two-phase RDE. The results show that reducing the hydrogen equivalence ratio and particle diameter both contribute to the transition from gas-phase single-front detonation to gas-solid two-phase double-front detonation and further to gas-solid two-phase single-front detonation. The effects of solid fuel particle diameter and hydrogen equivalence ratio on the flow field characteristics and performance are revealed. The results show that reducing the particle diameter enhances the speed of the two-phase detonation wave, improves the pressure gain in the combustion chamber, and increases the specific impulse. Decreasing the hydrogen equivalence ratio reduces the detonation wave speed, enhances the stability of the detonation flow field, increases the pressure gain in the detonation wave and combustion chamber and boosts thrust. Furthermore, the selection of operational conditions to ensure stable operation and optimal performance of the RDE is discussed. In order to take into account the requirements of stability, pressure gain performance and propulsion performance, two-phase single-front detonation should be realized in gas-solid two-phase RDE, and smaller hydrogen equivalent ratio and appropriate particle diameter should be selected. According to the conclusion of this study, the particle diameter should be 0.5-1 {\mu}m. Under such conditions, the detonation flow field demonstrates good stability, allowing the RDE to achieve higher pressure gain and specific impulse while maintaining stable operation

Mid-frequency prediction of transmission loss using a novel hybrid deterministic and statistical method

A novel hybrid deterministic-statistical approach named ES-FE-SEA method specially used to predict the sound Transmission loss of panels in mid-frequency is proposed in this paper. The proposed hybrid methods takes the best advantages of edged-based smoothing FEM (ES-FEM) and statistical energy analysis (SEA) to further improve the accuracy of mid-frequency transmission loss predictions. The application of ES-FEM will “soften” the well-known “overly-stiff” behavior in the standard FEM solution and reduce the inherent numerical dispersion error. While the SEA approach will deal with the physical uncertainty in the relatively higher frequency range. Two different types of subsystems will be coupled based on “reciprocity relationship” theorem. The proposed was firstly applied to a standard simple numerical example, and excellent agreement with reference results was achieved. Thus the method is then applied to a more complicated model-a 2D dash panel in a car. The proposed ES-FE-SEA is verified by various numerical examples

Hierarchical control strategy for unbalanced voltage in an islanded microgrid

When the microgrid is running in an islanded mode, unbalanced loads result in microgrid voltage unbalance. The voltage unbalance factor at the Point of Common Coupling (PCC) is a key parameter in measurement of microgrid power quality. To improve microgrid power quality, many documents utilize micro-source voltage measurement results to help adjust the unbalance factor of microgrid voltage. However, due to line impedance presence, there are differences between micro-source output voltage and PCC voltage. Therefore, it is impossible for a micro-source to control the unbalance factor of PCC voltage with high precision by measuring its own output voltage. Based on equivalent circuit, the present paper analyzes the negative sequence component relationship among micro-source output voltage, line impedance voltage drop, and PCC voltage. It further proposes a hierarchical-control-based method to control the unbalance factor of PCC voltage with high accuracy, and analyzes the impact of secondary control delay on system stability by root locus calculating. Finally, the control strategy is validated in an islanded microgrid system with two micro-sources. The experimental results show the effectiveness and feasibility of the proposed control strategy.Під час роботи мікроенергосистеми (МЕ) в ізольованому режимі незбалансовані навантаження призводять до дисбалансу напруги у ній. Фактор дисбалансу напруги у точці спільного приєднання (ТСП) є основним параметром при вимірюванні якості електроенергії МЕ. Для підвищення якості електроенергії МЕ використовують результати вимірювань напруги мікроджерел для врегулювання фактора дисбалансу напруги МЕ. Проте через наявність повного вхідного опору лінії існують відмінності між вихідною напругою мікрождерела та напругою ТСП. Тому мікроджерело не може контролювати фактор дисбалансу напруги ТСП з високою точністю шляхом вимірювання власної вихідної напруги. На базі еквівалентної схеми у даній статті аналізуються відношення складової оберненої послідовності між вихідною напругою мікроджерела, падінням напруги повного вхідного опору лінії та напругою у ТСП. Також для контролю фактора дисбалансу напруги ТСП із високою точністю пропонується метод на основі ієрархічного контролю, аналізується вплив затримки вторинного контролю на стабільність системи. Стратегія контролю перевірялася в ізольованій мікроенергосистемі з двома мікроджерелами. Дослідні дані показують ефективність та доцільність запропонованої стратегії контролю.При работе микроэнергосистемы (МЭ) в изолированном режиме несбалансированые нагрузки приводят к дисбалансу напряжения в ней. Фактор дисбаланса напряжения в точке общего присоединения (ТОП) является основным параметром при измерении качества электроэнергии МЭ. Для улучшения качества электроэнергии МЭ используют результаты измерений напряжения микроисточников для урегулирования дисбаланса напряжения МЭ. Однако из-за наличия полного входного сопротивления линии существуют различия между выходным напряжением микроисточника и напряжением ТОП. Поэтому микроисточник не может контролировать дисбаланс напряжения ТОП с высокой точностью путем измерения собственного выходного напряжения. На основании эквивалентной схемы в данной статье анализируется отношение составляющей обратной последовательности между выходным напряжением микроисточника, падением напряжения полного входного сопротивления линии и напряжением в ТОП. Также для контроля фактора дисбаланса напряжения ТОП с высокой точностью предлагается метод на основе иерархического контроля, анализируется влияние задержки вторичного контроля на стабильность системы. Стратегия контроля проверялась в изолированной микроэнергосистеме с двумя микроисточниками. Опытные данные показывают эффективность и целесообразность предлагаемой стратегии контроля

Light-Driven Spatiotemporal Pickering Emulsion Droplet Manipulation Enabled by Plasmonic Hybrid Microgels

The past decades have witnessed the development of various stimuli-responsive materials with tailored functionalities, enabling droplet manipulation through external force fields. Among different strategies, light exhibits excellent flexibility for contactless control of droplets, particularly in three-dimensional space. Here, we present a facile synthesis of plasmonic hybrid microgels based on the electrostatic heterocoagulation between cationic microgels and anionic Au nanoparticles. The hybrid microgels are effective stabilizers of oil-in-water Pickering emulsions. In addition, the laser irradiation on Au nanoparticles creats a “cascade effect” to thermally responsive microgels, which triggers a change in microgel wettability, resulting in microgel desorption and emulsion destabilization. More importantly, the localized heating generated by a focused laser induces the generation of a vapor bubble inside oil droplets, leading to the formation of a novel air-in-oil-in-water (A/O/W) emulsion. These A/O/W droplets are able to mimic natural microswimmers in an aqueous environment by tracking the motion of a laser spot, thus achieving on-demand droplet merging and chemical communication between isolated droplets. Such proposed systems are expected to extend the applications of microgel-stabilized Pickering emulsions for substance transport, programmed release and controlled catalytic reactions

CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graphs

Controllable scene synthesis aims to create interactive environments for various industrial use cases. Scene graphs provide a highly suitable interface to facilitate these applications by abstracting the scene context in a compact manner. Existing methods, reliant on retrieval from extensive databases or pre-trained shape embeddings, often overlook scene-object and object-object relationships, leading to inconsistent results due to their limited generation capacity. To address this issue, we present CommonScenes, a fully generative model that converts scene graphs into corresponding controllable 3D scenes, which are semantically realistic and conform to commonsense. Our pipeline consists of two branches, one predicting the overall scene layout via a variational auto-encoder and the other generating compatible shapes via latent diffusion, capturing global scene-object and local inter-object relationships while preserving shape diversity. The generated scenes can be manipulated by editing the input scene graph and sampling the noise in the diffusion model. Due to lacking a scene graph dataset offering high-quality object-level meshes with relations, we also construct SG-FRONT, enriching the off-the-shelf indoor dataset 3D-FRONT with additional scene graph labels. Extensive experiments are conducted on SG-FRONT where CommonScenes shows clear advantages over other methods regarding generation consistency, quality, and diversity. Codes and the dataset will be released upon acceptance

Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors

Absolute quantification of biological samples entails determining expression levels in precise numerical copies, offering enhanced accuracy and superior performance for rare templates. However, existing methodologies suffer from significant limitations: flow cytometers are both costly and intricate, while fluorescence imaging relying on software tools or manual counting is time-consuming and prone to inaccuracies. In this study, we have devised a comprehensive deep-learning-enabled pipeline that enables the automated segmentation and classification of GFP (green fluorescent protein)-labeled microreactors, facilitating real-time absolute quantification. Our findings demonstrate the efficacy of this technique in accurately predicting the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, thereby providing precise measurements of template concentrations. Notably, our approach exhibits an analysis speed of quantifying over 2,000 microreactors (across 10 images) within remarkably 2.5 seconds, and a dynamic range spanning from 56.52 to 1569.43 copies per micron-liter. Furthermore, our Deep-dGFP algorithm showcases remarkable generalization capabilities, as it can be directly applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based biological applications. To the best of our knowledge, this represents the first successful implementation of an all-in-one image analysis algorithm in droplet digital PCR (polymerase chain reaction), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without necessitating any transfer learning steps, modifications, or retraining procedures. We firmly believe that our Deep-dGFP technique will be readily embraced by biomedical laboratories and holds potential for further development in related clinical applications.Comment: 23 pages, 6 figures, 1 tabl

HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Pose Dataset with Household Objects in Realistic Scenarios

Estimating the 6D pose of objects is a major 3D computer vision problem. Since the promising outcomes from instance-level approaches, research heads also move towards category-level pose estimation for more practical application scenarios. However, unlike well-established instance-level pose datasets, available category-level datasets lack annotation quality and provided pose quantity. We propose the new category-level 6D pose dataset HouseCat6D featuring 1) Multi-modality of Polarimetric RGB and Depth (RGBD+P), 2) Highly diverse 194 objects of 10 household object categories including 2 photometrically challenging categories, 3) High-quality pose annotation with an error range of only 1.35 mm to 1.74 mm, 4) 41 large-scale scenes with extensive viewpoint coverage and occlusions, 5) Checkerboard-free environment throughout the entire scene, and 6) Additionally annotated dense 6D parallel-jaw grasps. Furthermore, we also provide benchmark results of state-of-the-art category-level pose estimation networks

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.This work is part of the ‘‘SpatioTemporal Omics Consortium’’ (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org. We would like to thank the MOTIC China Group, Rongqin Ke (Huaqiao University, Xiamen, China), Jiazuan Ni (Shenzhen University, Shenzhen, China), Wei Huang (Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China), and Jonathan S. Weissman (Whitehead Institute, Boston, USA) for their help. This work was supported by the grant of Top Ten Foundamental Research Institutes of Shenzhen, the Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011); Longqi Liu was supported by the National Natural Science Foundation of China (31900466) and Miguel A. Esteban’s laboratory at the Guangzhou Institutes of Biomedicine and Health by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), National Natural Science Foundation of China (92068106), and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075).S

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S