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High internal phase Pickering emulsions (HIPPEs) are versatile platforms for various applications owing to their low-density, solid-like structure, and large specific surface area. Here, naturally occurring polysaccharide-protein hybrid nanoparticles (PPH NPs) were used to stabilize HIPPEs with an internal phase fraction of 80% at a PPH NP concentration of 1.5%. The obtained HIPPEs displayed a gel-like behavior with excellent stability against centrifugation (10000g, 10 min), temperature (4–121 °C), pH (1.0–11.0), and ionic strength (0–500 mM). Confocal laser scanning microscope and cryo-scanning electron microscopy results showed that PPH NPs contributed to the stability of HIPPEs by effectively adsorbing and anchoring on the surface of the emulsion droplets layer by layer to form a dense 3D network barrier to inhibit droplet coalescence. The rheological analysis showed that the HIPPEs possessed a higher viscosity and lower frequency dependence with increasing PPH NP concentration, suggesting the potential application of such HIPPEs in three-dimensional (3D) printing, which was subsequently confirmed by a 3D printing experiment. This work provides highly stable and processable HIPPEs, which can be developed as facile and reusable materials for numerous applications. They can also be directly used for future food manufacturing, drug and nutrient delivery, and tissue reconstruction.

Pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently. However, as there exist numerous application scenarios that have distinctive demands such as certain latency constraints and specialized data distributions, it is prohibitively expensive to take advantage of large-scale pre-training for per-task requirements. we focus on two fundamental perception tasks (object detection and semantic segmentation) and present a complete and flexible system named GAIA-Universe(GAIA), which could automatically and efficiently give birth to customized solutions according to heterogeneous downstream needs through data union and super-net training. GAIA is capable of providing powerful pre-trained weights and searching models that conform to downstream demands such as hardware constraints, computation constraints, specified data domains, and telling relevant data for practitioners who have very few datapoints on their tasks. With GAIA, we achieve promising results on COCO, Objects365, Open Images, BDD100 k, and UODB which is a collection of datasets including KITTI, VOC, WiderFace, DOTA, Clipart, Comic, and more. Taking COCO as an example, GAIA is able to efficiently produce models covering a wide range of latency from 16 ms to 53 ms, and yields AP from 38.2 to 46.5 without whistles and bells. GAIA is released at https://github.com/GAIA-vision.

In order to overcome the low-detection accuracy of traditional methods, an artificial fish swarm algorithm was proposed to detect the energy consumption parameters of green and energy-saving buildings. The type of energy consumption equipment in green and energy-saving buildings is analysed, and the electricity consumption of building energy consumption equipment is taken as the building energy consumption parameter. The hierarchical clustering method was used to establish the classification model of energy consumption parameters, and the energy consumption parameters were classified and processed, and the energy consumption parameters detection model was built, and the preliminary detection results of energy consumption parameters were obtained. The artificial fish swarm algorithm was used to construct the optimisation function of building parameter detection results to obtain the optimal detection results of energy consumption parameters. Experimental results show that the accuracy of the proposed method is between 92.76% and 98.75%, and the practical application effect is good.

Fault diagnosis is important to stable operation of power systems, and the machine-learning-based fault-diagnosis models were widely studied because of their strong generalization ability. However, the model structures are generally designed according to the topology of power systems. Once there are changes in topology, the system fault characteristics might change, and the models structure and parameters are often required to be adjusted for applying to the new systems. To avoid frequent adjustment work of fault-diagnosis models when system topology changes, we propose a novel fault-diagnosis model for power systems. First, a new data preprocessing using gradient calculation and similarity assessment is presented, and the gradient similarities among the multichannel electrical signals are converted to the visualized similarity images, which are fed to the neural network for further processing. Second, the spatial pyramid pooling (SPP) and hashing classifier (HC) are used in the convolution neural network. With the aid of the SPP and HC techniques, the structure of the fault-diagnosis model can maintain unchanged even though there are topological changes in the power systems. To validate the effectiveness of the proposed model, several state-of-the-art fault-diagnosis models are used for comparison. The results show that the proposed model with unchanged structure is well performed in accuracy and noise immunity, and friendly to the parameters setting.

Fecal microbiota transplantation (FMT) by manual preparation has been applied to treat diseases for thousands of years. However, this method still endures safety risks and challenges the psychological endurance and acceptance of doctors, patients and donors. Population evidence showed the washed microbiota preparation with microfiltration based on an automatic purification system followed by repeated centrifugation plus suspension for three times significantly reduced FMT-related adverse events. This washing preparation makes delivering a precise dose of the enriched microbiota feasible, instead of using the weight of stool. Intraperitoneal injection in mice with the fecal microbiota supernatant obtained after repeated centrifugation plus suspension for three times induced less toxic reaction than that by the first centrifugation following the microfiltration. The toxic reactions that include death, the change in the level of peripheral white blood cells, and the proliferation of germinal center in secondary lymphoid follicles in spleen were noted. The metagenomic next-generation sequencing (NGS) indicated the increasing types and amount of viruses could be washed out during the washing process. Metabolomics analysis indicated metabolites with pro-inflammatory effects in the fecal microbiota supernatant such as leukotriene B4, corticosterone, and prostaglandin G2 could be removed by repeated washing. Near-infrared absorption spectroscopy could be served as a rapid detection method to control the quality of the washing-process. In conclusion, this study for the first time provides evidence linking clinical findings and animal experiments to support that washed microbiota transplantation (WMT) is safer, more precise and more quality-controllable than the crude FMT by manual.

Coalescence of conducting droplets dispersed in an immiscible medium can be facilitated by an electric field. However, droplets recoil promptly after contact in sufficiently high electric fields if the cone angle between droplets exceeds a critical value. To elucidate the critical condition for droplet coalescence, the behavior of two suspended droplets after contact with a direct current electric field is studied. It is shown that the critical angle is determined not only by the droplet geometry but also conductivity, surfactant concentration, and size. As the droplet conductivity increases, more identical ions accumulate on the adjacent interfaces of two droplets due to the faster ionic migration, which results in Coulombic repulsion between droplets and a reduced critical angle. For surfactant-laden droplets, film drainage induces a surfactant concentration gradient on the leading edges of droplets, and then Marangoni stress is formed to reduce the critical angle. In the case of large droplets, the bridge transiently expands under the action of directional flow caused by further droplet deformation, but eventually breaks due to opposite electrostatic forces. Based on this finding, the electrocoalescence criterion can be determined and employed to facilitate droplet coalescence in various applications.

In this study, the electrocoalescence characteristic of two drops with different surfactant concentrations suspended in oil is revealed. Different from the symmetrical coalescence of identical drops, the asymmetrical liquid bridge is induced by the capillary force, which involves the interfacial tension gradient between the drops. In addition, a mushroom-like jet is formed during asymmetrical coalescence, causing mixing of drops. A characteristic time considering the interfacial tension gradient is proposed to describe the asymmetrical liquid bridge evolution. The mushroom-like jet is found to be driven by the pressure difference between drops, which has a positive correlation with the interfacial tension gradient. Moreover, it is found that the electrocoalescence time is shortened as the interfacial tension increases. This study indicates that increasing the interfacial tension gradient will enhance the jet flow rate and reduce the coalescence time, which is of significance to improve the mixing efficiency of the drops.

Anode materials were vital for power production in microbial fuel cells (MFCs). By freeze-drying and carbonization, porous carbon monolith derived from wax gourd (WGCM) was prepared. WGCM was then coated by nano-Fe3O4to obtain the nano-Fe3O4/WGCM anode. WGCM anode increased the maximum power density of the MFCs from 430.6 mW/m2to 906.6 mW/m2, while nano-Fe3O4/WGCM anode further promoted it to 1438.8 mW/m2. WGCM had a three-dimensional (3D) microporous network structure, a hydrophilic surface and good conductivity, which enhanced biofilm formation and reduced internal resistance. Fe3O4increased Geobacterenrichment on anode surface, a popular electricigen in MFCs. Calculated from anode surface microbial cell density and the relative abundance, Geobacteron the nano-Fe3O4/WGCM increased by 41.4% compared with the WGCM anode. The results demonstrated that the preparation of carbon monolith coated with nano-Fe3O4was a promising method for anode enhancement in MFCs.