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Aimed at the poor accuracy in traditional deep drawing of aerospace aluminum alloy hemispherical shells, and by introducing metal spinning, a stamping-spinning hybrid process strategy for aluminum alloy hemispherical shells is proposed, which can achieve the forming processing of the formed component with a high thickness uniformity and high shape accuracy. The finite element simulation model of the hybrid process of the aluminum alloy hemispherical shell is developed, which realizes the simulation. In addition, the variation law of the wall thickness and shape fitting of the hemispherical shells formed by the hybrid process are analyzed. The simulation results show that the uniformity of wall thickness of the hemispherical shells formed by 50% stamping + 50% spinning is significantly improved. The shape accuracy of the component can be obviously improved by changing the stress state of the forming component by power spinning. Meanwhile, the hybrid process is verified by using an aluminum alloy hemispherical shell with a diameter of 1 m in processing test, which improves the thickness uniformity and the shape accuracy of the hemispherical shell.

Nematic liquid crystals (NLCs) offer remarkable opportunities to direct colloids to form complex structures. The elastic energy field that dictates colloid interactions is determined by the NLC director field, which is sensitive to and can be controlled by boundaries including vessel walls and colloid surfaces. By molding the director field via liquid-crystal alignment on these surfaces, elastic energy landscapes can be defined to drive structure formation. We focus on colloids in otherwise defect-free director fields formed near undulating walls. Colloids can be driven along prescribed paths and directed to well-defined docking sites on such wavy boundaries. Colloids that impose strong alignment generate topologically required companion defects. Configurations for homeotropic colloids include a dipolar structure formed by the colloid and its companion hedgehog defect or a quadrupolar structure formed by the colloid and its companion Saturn ring. Adjacent to wavy walls with wavelengths larger than the colloid diameter, spherical particles are attracted to locations along the wall with distortions in the nematic director field that complement those from the colloid. This is the basis of lock-and-key interactions. Here, we study ellipsoidal colloids with homeotropic anchoring near complex undulating walls. The walls impose distortions that decay with distance from the wall to a uniform director in the far field. Ellipsoids form dipolar defect configurations with the colloid's major axis aligned with the far field director. Two distinct quadrupolar defect structures also form, stabilized by confinement; these include the Saturn I configuration with the ellipsoid's major axis aligned with the far field director and the Saturn II configuration with the major axis perpendicular to the far field director. The ellipsoid orientation varies only weakly in bulk and near undulating walls. All configurations are attracted to walls with long, shallow waves. However, for walls with wavelengths that are small compared to the colloid length, Saturn II is repelled, allowing selective docking of aligned objects. Deep, narrow wells prompt the insertion of a vertical ellipsoid. By introducing an opening at the bottom of such a deep well, we study colloids within pores that connect two domains. Ellipsoids with different aspect ratios find different equilibrium positions. An ellipsoid of the right dimension and aspect ratio can plug the pore, creating a class of 2D selective membranes.

By confining soft materials within tailored boundaries it is possible to design energy landscapes to address and control colloidal dynamics. This provides unique opportunities to create reconfigurable, hierarchically organized structures, a leading challenge in materials science. Example soft matter systems include liquid crystals. For instance, when nematic liquid crystals (NLCs) are confined in a vessel with undulated boundaries, bend and splay distortions can be used to position particles. Here we confine this system in a twist cell. We also study cholesteric liquid crystals, which have an "intrinsic" twist distortion which adds to the ones imposed by the solid boundaries. The cholesteric pitch competes with the other length scales in the system (colloid radius, vessel thickness, wavelength of boundary undulations), enriching the possible configurations. Depending on the pitch-to-radius and pitch-to-thickness ratios the interaction can be attractive or repulsive. By tuning the pitch (i.e. changing the concentration of the chiral dopant), it is possible to selectively promote or inhibit particle trapping at the docking sites.

The ability to dictate the motion of microscopic objects is an important challenge in fields ranging from materials science to biology. Field-directed assembly drives microparticles along paths defined by energy gradients. Nematic liquid crystals, consisting of rod-like molecules, provide new opportunities in this domain. Deviations of nematic liquid crystal molecules from uniform orientation cost elastic energy, and such deviations can be molded by bounding vessel shape. Here, by placing a wavy wall in a nematic liquid crystal, we impose alternating splay and bend distortions, and define a smoothly varying elastic energy field. A microparticle in this field displays a rich set of behaviors, as this system has multiple stable states, repulsive and attractive loci, and interaction strengths that can be tuned to allow reconfigurable states. Microparticles can transition between defect configurations, move along distinct paths, and select sites for preferred docking. Such tailored landscapes have promise in reconfigurable systems and in microrobotics applications.

Energy harvesting is the process of converting external environmental energy into electrical energy. This technology has certain development potential in realizing self-powered low-power wireless sensors. At present, there are few researches on the adaptability of energy harvesting systems in terms of the application of energy harvesting technology in shears. Therefore, it is unable to achieve reliable applications. In order to solve the above problems, based on the characteristics of the working environment of shearer, this paper analyzes the feasibility of the weak environmental energy being harvested by shearer and the feasibility of converting the energy into electrical energy. It is pointed out that light energy, temperature difference heat energy and vibration energy are the three typical environmental energies that exist in shearer. Theses three energies have different adaptability due to different energy characteristics. Light energy has poor adaptability and is not suitable as an environmental energy source in energy harvesting technology. Temperature difference energy source is stable and the thermoelectric power generation device is easy to install. This energy has certain adaptability. However, the thermoelectric power generation device needs to be installed in the main heat producing part of shearer, and the installation location has certain restrictions. The total amount of vibration energy is large, and the piezoelectric power generation device has a simple structure. Vibration energy is less affected by the working environment factors and has a strong adaptability without installation location restrictions.

The effects of aging degradation on the safety performance of typical 2,4‐dinitroanisole (DNAN)‐based melt‐cast explosives (MCEs) were studied. An in‐house‐designed low‐velocity impact device and a closed bomb were used to study the effects of aging on the fragmentation and reactivity of DNAN‐based MCEs under impact damage. The test results showed that after artificially accelerated aging of the bare grain, the mass decreased, volume increased, and compressive strength increased, resulting in physical defects such as cracks and debonding occurring inside the explosive. The degree of damage to the DNAN‐based melt‐cast explosive increased with an increase in the impact velocity. Artificial aging increases the degree of damage and impact sensitivity of explosives. The relationship between the degree of fragmentation and the impact velocity under different aging times was revealed through the frangibility factor (FFE) and dynamic activity (L). The microcracks formed by DNAN sublimation and aging significantly affected the sample reaction violence. [ABSTRACT FROM AUTHOR]

Icephobic material is of great importance in power transportation, communication, aerospace, and so on. Bionic lotus superhydrophobic surfaces show good application prospects in anti‐icing by repelling impacting droplets. However, the boundary (criterion) between droplet bounce and deposition on superhydrophobic surface under cold freezing rain is unclear. Here, from the view of statistics, the boundary and internal heat transfer mechanism of the droplet bounce, pinning, and three kinds of deposition including icing at later, early retraction, and spread. are clarified Droplet viscosity increase reduces droplet contact with surface and inhibited splash at high We. Surfaces with temperature above −20.6 °C showed 100% droplet bounce after impact on We = 64.7, and the probability of water adhesion and ice nucleation decreases to ≈0%, resulting in completely anti‐icing. Droplet bounce transition mainly ascribe to the decrease retraction driven force caused by interface freezing and transition temperature increases approximately linearly from −25.9 to −21.1 °C within 21.6 < We < 75.5. Ice nuclei brought by condensation in humid environment (60–100 RH%) further cause droplet eccentric retraction and transition temperature increases to −6 to −10 °C. The fundamental understanding of droplet behaviors on supercooled superhydrophobic surface is beneficial for icephobic surfaces applications. [ABSTRACT FROM AUTHOR]

In this work, a simple anti‐fingerprint strategy (reduced ≈40–60%) to rearrange fingerprint droplets by patterned micro/nano‐pore/spoons texture, air cushion and low surface energy is reported. Superior to previous precise and complex preparation methods, the micro/nano‐texture are fabricated through conventional process of spray and dip. The pore and spoon can be freely switched by changing the process. The number (N) and size (r) of pores and spoons are controlled intelligently by solvent volatilization and interfacial tension (γFP/PU−H2O${{\bm{\gamma }}_{{\rm{FP/PU}}{\bm{ - }}{{\rm{H}}_2}{\rm{O}}}}$ and γFP/PU − Solvent), the key for pore/spoon's forming and expanding. A physical model of interface intelligent controlling the micro/nano‐pore/spoon surface texture induced by "Vacancy‐Capture" is proposed. The description is being reported, for the first time, to the knowledge, expecting to make it possible to prepare anti‐fingerprint engineering materials with newly potential industrial applications on optical device for large‐scale in the future. [ABSTRACT FROM AUTHOR]

Wetland emergent plants are considered a good source of biochar materials, which have proven to be an effective alternative to activated carbon for water treatment and soil amendment purposes. In this work, physicochemical properties of biochar made from the root, stem, and leaf of Zizania caduciflora (ZCR, ZCS, and ZCL) under the same pyrolysis condition were compared to evaluate their ability on adsorbing cadmium (Cd) in aqueous solutions. Batch experiments exhibited that the maximum adsorption capacities of Cd(II) by different organs were in the order of ZCL (59.8 mg/g) > ZCS (40.6 mg/g) > ZCR (36.2 mg/g), which have shown some advantages in relevant scientific researches. The adsorption trends at different temperatures were determined and fitted using Langmuir and Freundlich models. The best‐fitted adsorption isotherm was found to be the Langmuir isotherm. Two different kinetic models, pseudo‐first‐order and pseudo‐second‐order, were used to fit the kinetics data, with the latter best describing the experimental data (R2 > 0.989). Statistical analyses showed that ionic strength played an important role in controlling the sorption of Cd(II) onto the ZCR, ZCS, and ZCL. Concerning the mechanism, the adsorption of cadmium on biochar is recognized to be a chemical process as surface complexation and metal ions exchange, while it also exhibits some physisorption features due to the porous and defective structure. [ABSTRACT FROM AUTHOR]

Droplet bouncing on special wettability surface is common and important in daily life and engineering applications (anti‐icing and drag reduction, etc.). However, there are few studies on the multiple droplets impact and there is a lack of systematic research on the interaction of oscillation and bounce. Here, the impact and oscillation of multiple droplets on the porous superhydrophobic bionic lotus surface are studied by a high‐speed camera system. On the bionic lotus surface (Surf‐1), an impacting droplet can merge another larger sessile droplet and bounce off. Comparing with single droplet impact, it is suggested that multiple droplets impact make more efficient use of the air cushion on the micrometer‐nanometer‐scale binary structure (MNBS) and show the self‐cleaning performance of Surf‐1 better. Moreover, with the theory for damped harmonic oscillators, it is found that Surf‐1 can prolong the oscillation of droplets (last >250 ms) and slow down the attenuation of energy. The energy can be efficiently recycled to the bounce by shortening the droplet impact interval (ti). The fundamental understanding of multiple droplets impact and the reclamation of oscillating energy are beneficial for scientific design of new surface and engineering applications. [ABSTRACT FROM AUTHOR]

To study the influence of different laws of guide vane angle on the performance of reactor coolant pump (RCP), five models of RCP with different guide vane were designed, and the three‐dimensional numerical calculation was carried out. Based on the entropy production theory, analysis of flow loss at part‐load, design, and the over‐load flow rate was investigated. The results show that under the design flow and over‐load flow condition, the head, and efficiency of the DY4 (RCP model of the fourth guide vane variation) model are the highest, and the best efficiency occurs at the design flow rate. Compared with the DY1 (RCP model of the first guide vane variation) model, the efficiency of RCP is increased by 3.68%. At design flow and over‐load flow, the distribution of pressure and velocity streamlines of the DY4 model pump is uniform and stable, the flow loss ratio in the guide vane and volute is the lowest, and the high‐value area is small. Under the condition of part‐load flow rate, the pump head and efficiency of DY5 (RCP model of the fifth guide vane variation) model are the highest, and the highest efficiency appears at 0.8Qd which is 1.69% higher than the best efficiency value of DY4. At the part‐load flow rate, the static pressure values in the guide vane and volute of the DY5 model pump are significantly higher than that of other models. The velocity change in the flow channel is uniform, and the flow loss in the guide vane and volute is the lowest. Compared with the part‐load flow rate condition, the hydraulic performance of the DY5 model pump under design flow rate and over‐load flow condition is lower than that of other models. [ABSTRACT FROM AUTHOR]

Bionic self‐cleaning coatings have exhibited scaling inhibition for their low surface free energy. The key issue is focused on the long‐term anti‐scaling performance for the hydrophobicity failure after scaled. Inspired by "grass‐to‐stone" phenomenon, we reported a polymer coating (WCA = 120°, WSA = 14°) with the water contact angle of its 720 h‐scaled surface increased to 135°and scaling reduced by 90–95%. The nucleation and growth of CaCO3 at "coating‐crystal" interface are investigated, and a physical model for self‐healing film interface inhibiting scaling is innovatively proposed, as an important step toward designing long‐term anti‐scaling coating for engineering application. [ABSTRACT FROM AUTHOR]

Physically intelligent micro‐robotic systems exploit information embedded in micro‐robots, their colloidal cargo, and their milieu to interact, assemble, and form functional structures. Nonlinear anisotropic fluids such as nematic liquid crystals (NLCs) provide untapped opportunities to embed interactions via their topological defects, complex elastic responses, and ability to dramatically restructure in dynamic settings. Here a four‐armed ferromagnetic micro‐robot is designed and fabricated to embed and dynamically reconfigure information in the nematic director field, generating a suite of physical interactions for cargo manipulation. The micro‐robot shape and surface chemistry are designed to generate a nemato‐elastic energy landscape in the domain that defines multiple modes of emergent, bottom‐up interactions with passive colloids. Micro‐robot rotation expands the ability to sculpt interactions; the energy landscape around a rotating micro‐robot is dynamically reconfigured by complex far‐from‐equilibrium dynamics of the micro‐robot's companion topological defect. These defect dynamics allow transient information to be programmed into the domain and exploited. Robust micro‐robotic manipulation strategies are demonstrated that exploit these diverse modes of nemato‐elastic interaction to achieve cargo docking, transport, release, and assembly of complex reconfigurable structures at multi‐stable sites. Such structures are of great interest to future developments of LC‐based advanced optical device and micro‐manufacturing in anisotropic environments. [ABSTRACT FROM AUTHOR]

Porous bionic self‐cleaning surfaces with low contact time of droplets show good potentials in anti‐icing, drag reduction, antifouling, etc. However, the reason of asymmetric and fast retraction on inclined surfaces after droplet impact is not clear. Here, it is reported that the fast retraction is mainly ascribed to the "air cushion" in porous surface acting as "energy reservoir" that stores excess kinetic energy of droplet during spread and returns it back promoting droplets retraction with contact time 20–40% off. Besides, the pinning effect and wetting state transition result in the asymmetric morphology evolution and suddenly stretch along tangential. A physical model of droplet asymmetric retraction including the influence of dynamic wetting angle fDCA, pinning effect fPin, and air cushion fAir is innovatively proposed to describe droplet morphologic evolution. The fundamental understanding of droplets impact dynamic on inclined surfaces is beneficial for engineering applications of extremely wettable surfaces. [ABSTRACT FROM AUTHOR]

Purpose: Reducing X‐ray dose increases safety in cardiac electrophysiology procedures but also increases image noise and artifacts which may affect the discernibility of devices and anatomical cues. Previous denoising methods based on convolutional neural networks (CNNs) have shown improvements in the quality of low‐dose X‐ray fluoroscopy images but may compromise clinically important details required by cardiologists. Methods: In order to obtain denoised X‐ray fluoroscopy images whilst preserving details, we propose a novel deep‐learning‐based denoising framework, namely edge‐enhancement densenet (EEDN), in which an attention‐awareness edge‐enhancement module is designed to increase edge sharpness. In this framework, a CNN‐based denoiser is first used to generate an initial denoising result. Contours representing edge information are then extracted using an attention block and a group of interacted ultra‐dense blocks for edge feature representation. Finally, the initial denoising result and enhanced edges are combined to generate the final X‐ray image. The proposed denoising framework was tested on a total of 3262 clinical images taken from 100 low‐dose X‐ray sequences acquired from 20 patients. The performance was assessed by pairwise voting from five cardiologists as well as quantitative indicators. Furthermore, we evaluated our technique's effect on catheter detection using 416 images containing coronary sinus catheters in order to examine its influence as a pre‐processing tool. Results: The average signal‐to‐noise ratio of X‐ray images denoised with EEDN was 24.5, which was 2.2 times higher than that of the original images. The accuracy of catheter detection from EEDN denoised sequences showed no significant difference compared with their original counterparts. Moreover, EEDN received the highest average votes in our clinician assessment when compared to our existing technique and the original images. Conclusion: The proposed deep learning‐based framework shows promising capability for denoising interventional X‐ray fluoroscopy images. The results from the catheter detection show that the network does not affect the results of such an algorithm when used as a pre‐processing step. The extensive qualitative and quantitative evaluations suggest that the network may be of benefit to reduce radiation dose when applied in real time in the catheter laboratory. [ABSTRACT FROM AUTHOR]

Reducing radiation dose in cardiac catheter-based X-ray procedures increases safety but also image noise and artifacts. Excessive noise and artifacts can compromise vital image information, which can affect clinical decision-making. Developing more effective X-ray denoising methodologies will be beneficial to both patients and healthcare professionals by allowing imaging at lower radiation dose without compromising image information. This paper proposes a framework based on a convolutional neural network (CNN), namely Ultra-Dense Denoising Network (UDDN), for low-dose X-ray image denoising. To promote feature extraction, we designed a novel residual block which establishes a solid correlation among multiple-path neural units via abundant cross connections in its representation enhancement section. Experiments on synthetic additive noise X-ray data show that the UDDN achieves statistically significant higher peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) than other comparative methods. We enhanced the clinical adaptability of our framework by training using normally-distributed noise and tested on clinical data taken from procedures at St. Thomas’ hospital in London. The performance was assessed by using local SNR and by clinical voting using ten cardiologists. The results show that the UDDN outperforms the other comparative methods and is a promising solution to this challenging but clinically impactful task. [ABSTRACT FROM AUTHOR]

Two colloidal suspensions of paucidisperse, spherical silica particles with different surface chemistries leading to extreme limits of surface contact friction are studied to identify experimental differences in shear rheology and microstructure and quantitatively test theory and simulation models. The nonequilibrium microstructure in the plane of shear is measured by flow-small angle neutron scattering for steady shear states spanning the shear thinning and shear thickening regimes. The shear rheology and microstructure are compared against predictions from theory for Brownian hard sphere suspensions and state-of-the-art simulation methods that incorporate either contact friction or enhanced lubrication hydrodynamics. The first normal stress differences are confirmed to distinguish between these micromechanical mechanisms for stress enhancement in the shear thickened regime. The nonequilibrium microstructure in the plane of shear shows more anisotropy for the suspension with higher interparticle friction. A significant fourfold symmetry is confirmed and found to be amplified with increasing surface contact friction in the shear thickened state. The differences in shear-induced microstructures between suspensions with varying contact friction demonstrate that the nonequilibrium microstructure can distinguish between nanotribological interactions in the shear thickened state. Statistical comparison of experiments with simulations indicates that better resolution of microstructures in simulation models is required to be validated by the experimental data presented. Implications for the development of theories for colloidal suspension rheology are discussed. [ABSTRACT FROM AUTHOR]

Rheological experiments on model and industrial colloidal dispersions that exhibit reversible shear thickening are compared to a recent model that correlates simulation results for the shear rheology of bidisperse suspensions of particles with varying levels of contact friction. Comparisons are presented with rheological results for a variety of colloidal dispersions varying from model systems to industrially relevant, commercial dispersions where both shear and first normal stress differences are available. Particle-scale measurements of particle friction coefficients are provided through a survey of the literature. The comparisons show under which conditions the model can quantitatively describe the measured rheology and how the model can be used to correlate data for systems with more complex interactions when allowances are made for effective volume fractions and adjustments in friction coefficients. Discrepancies evident in comparisons for the first normal stress differences highlight the importance of enhanced hydrodynamic friction in many systems, especially for continuous shear thickening, in qualitative agreement with recent simulations that incorporate enhanced lubrication hydrodynamics due to elastohydrodynamics or particle roughness. Finally, a literature survey of measurements of jamming volume fractions and particle friction coefficients indicates that the model overestimates the experimentally observed jamming volume fraction. The comparisons presented in this work provide quantitative information valuable for those modeling suspension processing as well as suggestions for further model improvement. [ABSTRACT FROM AUTHOR]

Video satellite imagery is a new technique for earth dynamic observation and has a wide range of uses in environmental fields. Despite its capability of dynamic targets’ detection, it sustains a serious restriction of the image quality due to the degradation and compression in its imaging process. Hence, the super-resolution (SR) reconstruction on these compressed low-spatial-resolution images is of significance to afterward ground objects recognition and detection tasks. Based on the recent proposed state-of-the-art convolutional neural networks (CNNs) SR methods, we proposed an SR method which could get more precise reconstructed high-spatial-resolution images. Trained with Gaofen-2 satellite images, a robust CNN model specified in satellite image SR is obtained. Experimentally, the reconstruction results on Jilin-1 mission satellite images validate the effectiveness of our method. [ABSTRACT FROM PUBLISHER]

Both mesenchymal stromal cells (MSCs) and myeloid-derived suppressor cells (MDSCs) have immunosuppressive properties, and their presence may confer a worse prognosis upon cancer patients. However, whether MSCs can enhance the immunosuppressive effects of MDSCs in MM remains unknown. We evaluated the influence of MSCs on MDSCs growth, apoptosis, and functions. Our results show that MSCs promote proliferation and inhibit apoptosis in MDSCs. Additionally, MSCs enhance the ability of MDSCs by inhibiting T-cell proliferation and IFN-γ production. Furthermore, both the mRNA and protein levels of Arg1 and NOS2 were upregulated in MDSCs. These results suggest that MSCs may exert immunomodulatory effects on MDSCs by upregulating Arg1 and NOS2. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging (LiDAR) remote sensing enables accurate estimation and monitoring of terrain and vegetation, and digital surface model (DSM) and digital elevation model (DEM) are vital analytical tools to achieve this estimation and monitoring. Among them, DSM can be directly acquired from airborne LiDAR point clouds; nevertheless, for the production of DEM, point clouds representing a surface of ground objects should be accurately filtered out at first. In some mountain forest areas, due to the limited penetration of airborne LiDAR, ground points sustain a serious lack, which results in the difficulty in producing accurate DEMs. To reduce the intricacy and subjectivity caused by the manual supplement to ground points, this letter proposes a new DEM retrieval method from airborne LiDAR point clouds in mountain areas based on deep neural networks (DNNs). With a DNN model trained by accurate DEMs and DSMs, DEM retrieval becomes much easier by inputting their DSM into this model for prediction. Experiments on Fujian and Hainan mountain data sets demonstrate the effectiveness of this supervised method. [ABSTRACT FROM PUBLISHER]