Design of Locally Resonant Acoustic Metamaterials with Specified Band Gaps Using Multi-Material Topology Optimization.

Locally Resonant Acoustic Metamaterials (LRAMs) have significant application potential because they can form subwavelength band gaps. However, most current research does not involve obtaining LRAMs with specified band gaps, even though such LRAMs are significant for practical applications. To address this, we propose a parameterized level-set-based topology optimization method that can use multiple materials to design LRAMs that meet specified frequency constraints. In this method, a simplified band-gap calculation approach based on the homogenization framework is introduced, establishing a restricted subsystem and an unrestricted subsystem to determine band gaps without relying on the Brillouin zone. These subsystems are specifically tailored to model the phenomena involved in band gaps in LRAMs, facilitating the opening of band gaps during optimization. In the multi-material representation model used in this method, each material, except for the matrix material, is depicted using a similar combinatorial formulation of level-set functions. This model reduces direct conversion between materials other than the matrix material, thereby enhancing the band-gap optimization of LRAMs. Two problems are investigated to test the method's ability to use multiple materials to solve band-gap optimization problems with specified frequency constraints. The first involves maximizing the band-gap width while ensuring it encompasses a specified frequency range, and the second focuses on obtaining light LRAMs with a specified band gap. LRAMs with specified band gaps obtained in three-material or four-material numerical examples demonstrate the effectiveness of the proposed method. The method shows great promise for designing metamaterials to attenuate specified frequency spectra as required, such as mechanical vibrations or environmental noise.

Automatic Prostate Gleason Grading Using Pyramid Semantic Parsing Network in Digital Histopathology.

Purpose: Prostate biopsy histopathology and immunohistochemistry are important in the differential diagnosis of the disease and can be used to assess the degree of prostate cancer differentiation. Today, prostate biopsy is increasing the demand for experienced uropathologists, which puts a lot of pressure on pathologists. In addition, the grades of different observations had an indicating effect on the treatment of the patients with cancer, but the grades were highly changeable, and excessive treatment and insufficient treatment often occurred. To alleviate these problems, an artificial intelligence system with clinically acceptable prostate cancer detection and Gleason grade accuracy was developed. Methods: Deep learning algorithms have been proved to outperform other algorithms in the analysis of large data and show great potential with respect to the analysis of pathological sections. Inspired by the classical semantic segmentation network, we propose a pyramid semantic parsing network (PSPNet) for automatic prostate Gleason grading. To boost the segmentation performance, we get an auxiliary prediction output, which is mainly the optimization of auxiliary objective function in the process of network training. The network not only includes effective global prior representations but also achieves good results in tissue micro-array (TMA) image segmentation. Results: Our method is validated using 321 biopsies from the Vancouver Prostate Centre and ranks the first on the MICCAI 2019 prostate segmentation and classification benchmark and the Vancouver Prostate Centre data. To prove the reliability of the proposed method, we also conduct an experiment to test the consistency with the diagnosis of pathologists. It demonstrates that the well-designed method in our study can achieve good results. The experiment also focused on the distinction between high-risk cancer (Gleason pattern 4, 5) and low-risk cancer (Gleason pattern 3). Our proposed method also achieves the best performance with respect to various evaluation metrics for distinguishing benign from malignant. Availability: The Python source code of the proposed method is publicly available at https://github.com/hubutui/Gleason. All implementation details are presented in this paper. Conclusion: These works prove that the Gleason grading results obtained from our method are effective and accurate.

VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images.

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.

EIA metamaterials based on hybrid metal/dielectric structures with dark-mode-enhanced absorption.

Metamaterial analogue of electromagnetically induced absorption (EIA) has promising applications in spectroscopy and sensing. Here we propose an EIA metamaterial based on hybrid metal/dielectric structures, which are composed of a metallic wire and a dielectric block, and investigate the EIA-like effect by simulations, experiments, and the two-oscillator model. An EIA-like effect emerges in virtue of the near-field coupling between metallic wire and dielectric block, and the dielectric block exhibiting magnetic dipolar resonance makes a major contribution to the resonance absorption. The magnetic flux through the dielectric block engendered by the near filed of the metallic wire determines the coupling between dielectric block and metallic wire. With the variation of the separation between dielectric block and metallic wire, the EIA-like effect is preserved and does not convert into the EIT-like effect although the coupling and consequently the absorbance are altered. Based on the two-oscillator model, the absorption spectrum of the EIA metamaterial is quantitatively analyzed and the parameters of the oscillator system are retrieved.

Importance of Interface in the Coarse-Grained Model of CNT /Epoxy Nanocomposites.

Interface interactions play a crucial role in determining the thermomechanical properties of carbon nanotubes (CNTs)/polymer nanocomposites. They are, however, poorly treated in the current multi-scale coarse-grained (CG) models. To develop suitable CG models of CNTs/polymer nanocomposites, we demonstrate the importance of two aspects for the first time, that is, preserving the interfacial cohesive energy and reproducing the interface load transfer behavior of all-atomistic (AA) systems. Our simulation results indicate that, for CNTs/polymer nanocomposites, the interface cohesive energy and the interface load transfer of CG models are generally inconsistent with their AA counterparts, revealing significant deviations in their predicted mechanical properties. Fortunately, such inconsistency can be "corrected" by phenomenologically adjusting the cohesive interaction strength parameter of the interface LJ potentials in conjunction with choosing a reasonable degree of coarse-graining of incorporated CNTs. We believe that the problem studied here is general for the development of the CG models of nanocomposites, and the proposed strategy used in present work may be applied to polymer nanocomposites reinforced by other nanofillers.

Fully Automatic Pediatric Echocardiography Segmentation Using Deep Convolutional Networks Based on BiSeNet.

Accurate segmentation of pediatric echocardiography is an essential preprocessing step for a wide range of analysis tasks. Currently, it highly relies on sonographer's manual segmentation, which is time-consuming and redundant, and therefore might lead to mistakes. In this paper, we present a deep learning method based on Bilateral Segmentation Network (BiSeNet) to fully automatic segment pediatric echocardiography images in 4 chamber view. BiSeNet consists of two paths, a spatial path for capturing low-level spatial features, and a context path for exploiting high-level context semantic features. In addition, a feature fusion module is used to fuse features learned by both the two paths. Experiments based on our selfcollected dataset shows that our method achieves 0.932, and 0.908 in term of Dice index in the left ventricle and left atrium segmentation task, which outperforms different state-of-the-art U-Net architectures.

Diamond nanothread based resonators: ultrahigh sensitivity and low dissipation.

The recently synthesized ultrathin diamond nanothreads (NTHs) exhibit a variety of intriguing properties and are probably the most successful of many encouraging applications to be designed as resonators due to their ultrahigh sensitivity and low dissipation. Herein, we report via molecular dynamics that diamond nanothreads possess not only ultrahigh mass sensitivity but also a very high quality factor. On the one hand, the studied diamond nanothreads demonstrate an extreme mass resolution of ∼0.58 yg (1 yg = 10-24 g), which is almost one order of magnitude higher than that of carbon nanotubes (∼10 yg) with the same length. Moreover, the sensing performance of NTHs is highly tunable owing to their tailorable structures. On the other hand, NTHs exhibit a very low intrinsic energy dissipation and thus a high quality factor which is generally two times that of carbon nanotubes. These intriguing features suggest that diamond nanothreads could be highly attractive candidates for fabricating nano-sized mechanical resonators with outstanding performance.

Pillared graphene as an ultra-high sensitivity mass sensor.

Hybrid structure of graphene sheets supported by carbon nanotubes (CNTs) sustains unique properties of both graphene and CNTs, which enables the utilization of advantages of the two novel materials. In this work, the capability of three-dimensional pillared graphene structure used as nanomechanical sensors is investigated by performing molecular dynamics simulations. The obtained results demonstrate that: (a) the mass sensitivity of the pillared graphene structure is ultrahigh and can reach at least 1 yg (10-24 g) with a mass responsivity 0.34 GHz · yg-1; (b) the sizes of pillared graphene structure, particularly the distance between carbon nanotube pillars, have a significant effect on the sensing performance; (c) an analytical expression can be derived to detect the deposited mass from the resonant frequency of the pillared graphene structure. The performed analyses might be significant to future design and application of pillared graphene based sensors with high sensitivity and large detecting area.

Finite element modelling of human auditory periphery including a feed-forward amplification of the cochlea.

A three-dimensional finite element model is developed for the simulation of the sound transmission through the human auditory periphery consisting of the external ear canal, middle ear and cochlea. The cochlea is modelled as a straight duct divided into two fluid-filled scalae by the basilar membrane (BM) having an orthotropic material property with dimensional variation along its length. In particular, an active feed-forward mechanism is added into the passive cochlear model to represent the activity of the outer hair cells (OHCs). An iterative procedure is proposed for calculating the nonlinear response resulting from the active cochlea in the frequency domain. Results on the middle-ear transfer function, BM steady-state frequency response and intracochlear pressure are derived. A good match of the model predictions with experimental data from the literatures demonstrates the validity of the ear model for simulating sound pressure gain of middle ear, frequency to place map, cochlear sensitivity and compressive output for large intensity input. The current model featuring an active cochlea is able to correlate directly the sound stimulus in the ear canal with the vibration of BM and provides a tool to explore the mechanisms by which sound pressure in the ear canal is converted to a stimulus for the OHCs.

Finite element analysis of the coupling between ossicular chain and mass loading for evaluation of implantable hearing device.

Finite element (FE) model is used to analyze the coupling effects between ossicular chain and transducer of implantable middle-ear hearing devices. The mass loading of the transducer is attached to the long process of the incus in the form of floating mass transducer (FMT) or applied near the incus-stapes joint by a magnet of contactless electromagnetic transducer (CLT). By changing placement of the transducer, crimping connection and damping parameter of the crimping mechanism, theoretical performances of the transducers were investigated on mechanical characteristics in two aspects: (1) displacement change at the stapes footplate, which describes the change in hearing due to placement of the transducer; (2) the equivalent pressure output of the transducer, which relates the footplate displacement driven by transducer to the sound pressure applied to a normal ear to produce that displacement. For the FMT with a less tight crimping connection or low supporting rigidity, a large drop of the sound-induced stapes displacement occurs at a specific frequency, with a peak reduction about 25.8 dB. A tight connection or high supporting rigidity shifts the drop of the stapes displacement to higher frequency. For the CLT, an electromagnetic transducer of 25 mg placed near the incus-stapes joint produces a maximum decrease of the stapes displacement around 16.5 dB. The equivalent sound pressure output and electromagnetic force requirement are proposed to produce the stapes displacement equivalent to that ear canal sound stimulus. The drop of the footplate displacement caused by mass loading effect can be recovered by the transducer stimulation over frequency range from 1500 Hz to 4000 Hz. The FE analysis reveals that enhancing the coupling stiffness between the clip and the ossicular chain is much helpful for maximizing the efficiency of the transducer stimulation.