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The aim of this study is to investigate the effect of particles as filler in soft rubber sensors installed in artificial skin. We examine sensors made of natural rubber (NR-latex) that include magnetic particles of Ni and Fe3O4 using magnetic compound fluid (MCF). The 1-mm thickness of the electrolytically polymerized MCF rubber makes production of comparatively thin rubber sensors feasible. We first investigate the effect of magnetic particles Ni and Fe3O4 on the curing of MCF rubber. Next, in order to adjust the electric properties of the MCF rubber, we adopt Al2O3 dielectric particles. We investigate the effect of Al2O3 particles on changes in electric current, voltage and temperature of electrolytically polymerized MCF rubber liquid, and on the electric properties under the application of normal and shear forces. By adjusting the ratio of Ni, Fe3O4, Al2O3 and water in MCF rubber with Al2O3, it is possible to change the electric properties.

Magnetorheological (MR) fluid finishing process is an application of MR technology in which controllability of the MR fluid is used advantageously to finish the workpiece surface. MR finishing fluid changes its stiffness in accordance with the applied magnetic field and hence it behaves like a flexible finishing tool. A relative motion between this tool and workpiece removes the material from the machining surface. The quality of the final finished surface depends on the constituents of the finishing fluid and the applied magnetic field strength as these parameters affect the rheological properties of the fluid. A study on the rheological properties of the fluid at high shear rates is carried out through Taguchi Design of Experiments to characterize its flow behaviour to be used in continuous flow finishing process. Constitutive modeling of the fluid sample is done using Bingham Plastic, Casson Fluid and Herschel Bulkley fluid models to characterize their rheological behavior. The Hershel–Bulkley model is found to be the best suited model for the finishing fluid. Analysis of Variance has revealed that volume percentage of iron particles is the most significant parameter with a contribution of 91.68% on the yield stress and viscosity on the finishing fluid. The highest yield stress of the fluid is observed between magnetic flux density ranges from 0.3 to 0.5 Tesla. An optimised combination is then synthesized to confirm the theoretical results. The effect of temperature is also studied on the optimised fluid which has shown that temperature shares an inverse relation with the yield stress of the finishing fluid.

The novel controllable behaviour of magnetorheological (MR) fluid is the backbone of magnetorheological fluid-based finishing processes. MR fluid-based finishing processes facilitate better control over finishing forces as the stiffness of MR finishing fluid used in these processes can be controlled in accordance with the applied magnetic field and MR finishing fluid composition. Therefore, a detailed experimental investigation was carried out to find the effect of MR finishing fluid constituents on its yield stress through the Taguchi Design of Experiments. Rheological data obtained from a magneto-rheometer (MCR-102) was characterised by using Bingham plastic, Herschel–Bulkley and Casson’s fluid constitutive modelling. The coefficient of regression (R2) values of Herschel–Bulkley model were found to be best suited for all compositions of MR finishing fluid. Analysis of variance (ANOVA) has been used to find the contribution of selected parameters for improving the response characteristics. The optimized fluid has been then used for the finishing of biocompatible stainless steel AISI 316L, and the finishing results show that the average surface roughness value decreases down to 58 nm.

Self-assembly is a process in which interacting bodies are autonomously driven into ordered structures. Static structures such as crystals often form through simple energy minimization, whereas dynamic ones require continuous energy input to grow and sustain. Dynamic systems are ubiquitous in nature and biology but have proven challenging to understand and engineer. Here, we bridge the gap from static to dynamic self-assembly by introducing a model system based on ferrofluid droplets on superhydrophobic surfaces. The droplets self-assemble under a static external magnetic field into simple patterns that can be switched to complicated dynamic dissipative structures by applying a time-varying magnetic field. The transition between the static and dynamic patterns involves kinetic trapping and shows complexity that can be directly visualized.

Under the background of the Yangtze River Protection Initiative, it is imperative to achieve breakthroughs in the advanced treatment technology for low-concentration phosphorus-containing water. The exploration and development of new-generation, high-efficiency phosphorus removal technologies holds the key to the elevation of water environment quality. A newly-designed magnetic flocculation apparatus has been engineered. Through the addition of polyaluminium chloride (PAC) and magnetic powder, the flocculated sewage was directed to traverse the stainless-steel magnetic recovery channel with a hollow configuration. Thereby, the effluent discharged from the sewage treatment plant was purified and the removal efficiency of total phosphorus in the sewage was amplified. By conducting in-depth inquiries into the classifications of flocculants, magnetic field strength, the proportion and dosing quantity of flocculant addition, as well as comprehensive on-site pilot trials that were carried out, a meticulous examination of the removal effect of this state-of-the-art magnetic flocculation device on sewage was performed. The findings revealed that when the PAC concentration stood at 30 mg/L and the magnetic powder dosage hit 90 mg/L, in tandem with the application of a suitable external magnetic field, the total phosphorus removal rate peaked at 78%. This study demonstrates that the proposed technology offers an efficient and promising solution for advanced phosphorus removal in wastewater treatment plants.

Magnetometer data acquired by the MESSENGER spacecraft in orbit about Mercury permit the separation of internal and external magnetic field contributions. The global planetary field is represented as a southward-directed, spin-aligned, offset dipole centered on the spin axis. Positions where the cylindrical radial magnetic field component vanishes were used to map the magnetic equator and reveal an offset of 484 ± 11 kilometers northward of the geographic equator. The magnetic axis is tilted by less than 3° from the rotation axis. A magnetopause and tail-current model was defined by using 332 magnetopause crossing locations. Residuals of the net external and offset-dipole fields from observations north of 30°N yield a best-fit planetary moment of 195 ± 10 nanotesla- ${\\mathrm{R}}_{\\mathrm{M}}^{3}\\phantom{\\rule{0ex}{0ex}}$ , where R M is Mercury's mean radius.

The Sun's meridional flow is an axisymmetric flow that is generally directed from its equator toward its poles at the surface. The structure and strength of the meridional flow determine both the strength of the Sun's polar magnetic field and the intensity of sunspot cycles. We determine the meridional flow speed of magnetic features on the Sun using data from the Solar and Heliospheric Observatory. The average flow is poleward at all latitudes up to 75°, which suggests that it extends to the poles. It was faster at sunspot cycle minimum than at maximum and substantially faster on the approach to the current minimum than it was at the last solar minimum. This result may help to explain why this solar activity minimum is so peculiar.

Galaxy clusters form through a sequence of mergers of smaller galaxy clusters and groups. Models of diffusive shock acceleration suggest that in shocks that occur during cluster mergers, particles are accelerated to relativistic energies, similar to conditions within supernova remnants. In the presence of magnetic fields, these particles emit synchrotron radiation and may form so-called radio relics. We detected a radio relic that displays highly aligned magnetic fields, a strong spectral index gradient, and a narrow relic width, giving a measure of the magnetic field in an unexplored site of the universe. Our observations show that diffusive shock acceleration also operates on scales much larger than in supernova remnants and that shocks in galaxy clusters are capable of producing extremely energetic cosmic rays.

Magnetic fields measured by Voyager 1 (V1) show that the spacecraft crossed the boundary of an unexpected region five times between days 210 and ~238 in 2012. The magnetic field strength B increased across this boundary from ≈0.2 to ≈0.4 nanotesla, and B remained near 0.4 nanotesla until at least day 270, 2012. The strong magnetic fields were associated with unusually low counting rates of >0.5 mega–electron volt per nuclear particle. The direction of B did not change significantly across any of the five boundary crossings; it was very uniform and very close to the spiral magnetic field direction, which was observed throughout the heliosheath. The observations indicate that V1 entered a region of the heliosheath (the heliosheath depletion region), rather than the interstellar medium.

Background Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer’s disease (AD) classification performance. Methods T1-weighted brain MRI scans from 151 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n  = 107) and the National Alzheimer’s Coordinating Center (NACC, n  = 565) were used for model validation. Results The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets. Conclusion This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality.