Symmetric Surface Research Articles

Examining heat transfer of a buried pipe heat exchanger in arch tunnel lining with protection engineering

Cooling towers and internal reservoirs are two conventional heat transfer methods used in protection engineering. However, they have the drawbacks of easy exposure and limited capacity. Although the traditional ground source heat pump system can become a new heat transfer method, its high installation cost and space requirements are obstacles to the development of this technology. To address this problem, the heat exchange pipe of the ground source heat pump system being directly laid in the tunnel lining was proposed in this study. A heat transfer performance test platform for a lined buried pipe heat exchanger based on an arch tunnel was built, and the water velocity, wind velocity, and temperature of each measuring point were recorded under appropriate working conditions. The effects of the inlet water temperature, inlet water velocity, ventilation velocity, and initial soil temperature on the heat transfer performance were analyzed. The results have shown that the heat transfer performance of the tunnel reached its maximum when system operation commenced and then decreased gradually with time, and the rate of decrease gradually slowed. Along the radial direction of the tunnel, the shorter the distance to the buried heat exchanger, the greater the increase in soil temperature, and the shorter the time for the temperature to reach the peak value. As the distance increased, the temperature gradient gradually decreased. Along the circumferential direction of the tunnel, the lining temperature on the inlet side of the buried heat exchanger was higher than that on the outlet side. The temperature distribution on both sides of the symmetrical surface of the tunnel was not uniform, resulting in additional temperature stress in the horizontal direction. The time required for the heat transfer capacity of the tunnel to return to its initial value was approximately eight times that of the system. Compared with the ventilation and water velocities, the inlet water temperature and initial soil temperature had a greater impact on heat transfer. The heat-transfer law of an arch tunnel differs from that of a circular tunnel. There were some deviations when comparing the experimental data of the circular tunnel with those of the actual arch tunnel.

Diffusion MRI of the facial-vestibulocochlear nerve complex: a prospective clinical validation study

ObjectivesSurgical planning of vestibular schwannoma surgery would benefit greatly from a robust method of delineating the facial-vestibulocochlear nerve complex with respect to the tumour. This study aimed to optimise a multi-shell readout-segmented diffusion-weighted imaging (rs-DWI) protocol and develop a novel post-processing pipeline to delineate the facial-vestibulocochlear complex within the skull base region, evaluating its accuracy intraoperatively using neuronavigation and tracked electrophysiological recordings.MethodsIn a prospective study of five healthy volunteers and five patients who underwent vestibular schwannoma surgery, rs-DWI was performed and colour tissue maps (CTM) and probabilistic tractography of the cranial nerves were generated. In patients, the average symmetric surface distance (ASSD) and 95% Hausdorff distance (HD-95) were calculated with reference to the neuroradiologist-approved facial nerve segmentation. The accuracy of patient results was assessed intraoperatively using neuronavigation and tracked electrophysiological recordings.ResultsUsing CTM alone, the facial-vestibulocochlear complex of healthy volunteer subjects was visualised on 9/10 sides. CTM were generated in all 5 patients with vestibular schwannoma enabling the facial nerve to be accurately identified preoperatively. The mean ASSD between the annotators’ two segmentations was 1.11 mm (SD 0.40) and the mean HD-95 was 4.62 mm (SD 1.78). The median distance from the nerve segmentation to a positive stimulation point was 1.21 mm (IQR 0.81–3.27 mm) and 2.03 mm (IQR 0.99–3.84 mm) for the two annotators, respectively.Conclusionsrs-DWI may be used to acquire dMRI data of the cranial nerves within the posterior fossa.Clinical relevance statementReadout-segmented diffusion-weighted imaging and colour tissue mapping provide 1–2 mm spatially accurate imaging of the facial-vestibulocochlear nerve complex, enabling accurate preoperative localisation of the facial nerve. This study evaluated the technique in 5 healthy volunteers and 5 patients with vestibular schwannoma.Key Points• Readout-segmented diffusion-weighted imaging (rs-DWI) with colour tissue mapping (CTM) visualised the facial-vestibulocochlear nerve complex on 9/10 sides in 5 healthy volunteer subjects.• Using rs-DWI and CTM, the facial nerve was visualised in all 5 patients with vestibular schwannoma and within 1.21–2.03 mm of the nerve’s true intraoperative location.• Reproducible results were obtained on different scanners.

Experimental Demonstration of Low‐Energy First‐Order Hybridized Plasmon Resonances in Origami Metashells

AbstractMetallic nanoshells have emerged as promising optical cavities to steer hybridized plasmonic resonances that show the high spectral tunability and a large surface area of localized fields. Despite various optical and photochemical applications, plasmonic resonances in shells have rarely been explored at low frequencies due to a nonlinear increase in particle size and a rapid degeneration in field localization. Here, this work reports the experimental realization of hybridized plasmon resonances in origami metashells that can be excited by low‐energy photons ≈7 GHz, corresponding to a level below 10−4 eV. Metasurfaces built of interconnected meshes act as thin Drude metallic films that support symmetric spoof surface plasmons. Rigid folding of the metasurface leads to diverse 3D hollow cavities that exhibit strong plasmonic responses, large surface areas, and low relative densities. Experimental measurements demonstrate three typical behaviors in Drude metallic shells: shielding to quasi‐static fields, strong scattering at first‐order hybridized plasmon resonances, and transparency to high frequency radiation. The relative density is remarkably reduced owing to the void cavity enabled by 3D folding, and an extra degree of freedom in anisotropic load capacity is provided by customizing crease patterns. These results open a door for extremely low‐energy plasmons with low‐density mechanics.

Electrokinetic cells powered by osmotic gradients: an analytic survey of asymmetric wall potentials and hydrophobic surfaces

Nowadays, the fabrication of microelectromechanical systems has given rise to several studies whose main purpose is to obtain the greater benefit of micro-nano scales, putting special interest in the improvement of the design of such devices. One of several applications is harvesting energy due to electrokinetic phenomena, more specifically, streaming potential. Nonetheless, there is a lack of theoretical studies encompassing coupled asymmetries in both slip conditions and electric potentials (these being associated with the chemical and physical characteristics of the surfaces). By virtue of the previous explanation, ideal assumptions based on the symmetry of some variables must be reconsidered, especially when manufacturing symmetric flat surfaces on a tiny scale is quite difficult to achieve. This work presents a theoretical study in power generation, exploiting streaming potentials produced by an asymmetric membrane which in turn prompts a flux inside a microchannel made of two flat parallel surfaces. The driving force in this electrokinetic battery is the osmotic gradient on both sides of the membrane. The model uses the Debye–Hückel approximation together with the appropriate asymmetric boundary conditions for both slips and potentials on the surfaces. The main variables of interest, such as the dimensionless horizontal velocity component, the pressure field, and the average streaming potential, were estimated.

Ergodicity of the geodesic flow on symmetric surfaces

We consider conditions on the Fenchel-Nielsen parameters of a Riemann surface <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper X"> <mml:semantics> <mml:mi>X</mml:mi> <mml:annotation encoding="application/x-tex">X</mml:annotation> </mml:semantics> </mml:math> </inline-formula> that guarantee the surface <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper X"> <mml:semantics> <mml:mi>X</mml:mi> <mml:annotation encoding="application/x-tex">X</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is of parabolic type. An interesting class of Riemann surfaces for this problem is the one with finitely many topological ends. In this case the length part of the Fenchel-Nielsen coordinates can go to infinity for parabolic <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper X"> <mml:semantics> <mml:mi>X</mml:mi> <mml:annotation encoding="application/x-tex">X</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. When the surface <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper X"> <mml:semantics> <mml:mi>X</mml:mi> <mml:annotation encoding="application/x-tex">X</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is end symmetric, we prove that <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper X"> <mml:semantics> <mml:mi>X</mml:mi> <mml:annotation encoding="application/x-tex">X</mml:annotation> </mml:semantics> </mml:math> </inline-formula> being parabolic is equivalent to the covering group being of the first kind. Then we give necessary and sufficient conditions on the Fenchel-Nielsen coordinates of a half-twist symmetric surface <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper X"> <mml:semantics> <mml:mi>X</mml:mi> <mml:annotation encoding="application/x-tex">X</mml:annotation> </mml:semantics> </mml:math> </inline-formula> such that <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper X"> <mml:semantics> <mml:mi>X</mml:mi> <mml:annotation encoding="application/x-tex">X</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is parabolic. As an application, we solve an open question from the prior work of Basmajian, Hakobyan and the second author [Proc. Lond. Math. Soc. (3) 125 (2022), pp. 568–625].

Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network.

The aim of this study was to improve the accuracy of the clinical target volume (CTV) and organs at risk (OARs) segmentation for rectal cancer preoperative radiotherapy. Computed tomography (CT) scans from 265 rectal cancer patients treated at our institution were collected to train and validate automatic contouring models. The regions of CTV and OARs were delineated by experienced radiologists as the ground truth. We improved the conventional U-Net and proposed Flex U-Net, which used a register model to correct the noise caused by manual annotation, thus refining the performance of the automatic segmentation model. Then, we compared its performance with that of U-Net and V-Net. The Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD) were calculated for quantitative evaluation purposes. With a Wilcoxon signed-rank test, we found that the differences between our method and the baseline were statistically significant (P< 0.05). Our proposed framework achieved DSC values of 0.817 ± 0.071, 0.930 ± 0.076, 0.927 ± 0.03, and 0.925 ± 0.03 for CTV, the bladder, Femur head-L and Femur head-R, respectively. Conversely, the baseline results were 0.803 ± 0.082, 0.917 ± 0.105, 0.923 ± 0.03 and 0.917 ± 0.03, respectively. In conclusion, our proposed Flex U-Net can enable satisfactory CTV and OAR segmentation for rectal cancer and yield superior performance compared to conventional methods. This method provides an automatic, fast and consistent solution for CTV and OAR segmentation and exhibits potential to be widely applied for radiation therapy planning for a variety of cancers.

Fermi surface symmetric mass generation

Symmetric mass generation is a novel mechanism to give gapless fermions a mass gap by non-perturbative interactions without generating any fermion bilinear condensation. The previous studies of symmetric mass generation have been limited to Dirac/Weyl/Majorana fermions with zero Fermi volume in the free fermion limit. In this work, we generalize the concept of symmetric mass generation to Fermi liquid with a finite Fermi volume and discuss how to gap out the Fermi surfaces by interactions without breaking the U(1) loop group symmetry or developing topological orders. We provide examples of Fermi surface symmetric mass generation in both (1+1)D and (2+1)D Fermi liquid systems when several Fermi surfaces together cancel the Fermi surface anomaly. However, the U(1) loop group symmetry in these cases is still restrictive enough to rule out all possible fermion bilinear gapping terms, such that a non-perturbative interaction mechanism is the only way to gap out the Fermi surfaces. This symmetric Fermi surface reconstruction is in contrast to the conventional symmetry-breaking mechanism to gap the Fermi surfaces. As a side product, our model provides a pristine 1D lattice regularization for the (1+1)D U(1) symmetric chiral fermion model (e.g., the 3-4-5-0 model) by utilizing a lattice translation symmetry as an emergent U(1) symmetry at low energy. This opens up the opportunity for efficient numerical simulations of chiral fermions in their own dimensions without introducing mirror fermions under the domain wall fermion construction.

A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI)

Accurate and efficient delineation of the clinical target volume (CTV) is of utmost significance in post-operative breast cancer radiotherapy. However, CTV delineation is challenging as the exact extent of microscopic disease encompassed by CTV is not visualizable in radiological images and remains uncertain. We proposed to mimic physicians’ contouring practice for CTV segmentation in stereotactic partial breast irradiation (S-PBI) where CTV is derived from tumor bed volume (TBV) via a margin expansion followed by correcting the extensions for anatomical barriers of tumor invasion (e.g. skin, chest wall). We proposed a deep-learning model, where CT images and the corresponding TBV masks formed a multi-channel input for a 3D U-Net based architecture. The design guided the model to encode the location-related image features and directed the network to focus on TBV to initiate CTV segmentation. Gradient weighted class activation map (Grad-CAM) visualizations of the model predictions revealed that the extension rules and geometric/anatomical boundaries were learnt during model training to assist the network to limit the expansion to a certain distance from the chest wall and the skin. We retrospectively collected 175 prone CT images from 35 post-operative breast cancer patients who received 5-fraction partial breast irradiation regimen on GammaPod. The 35 patients were randomly split into training (25), validation (5) and test (5) sets. Our model achieved mean (standard deviation) of 0.94 (±0.02), 2.46 (±0.5) mm, and 0.53 (±0.14) mm for Dice similarity coefficient, 95th percentile Hausdorff distance, and average symmetric surface distance respectively on the test set. The results are promising for improving the efficiency and accuracy of CTV delineation during on-line treatment planning procedure.

Segment aorta and localize landmarks simultaneously on noncontrast CT using a multitask learning framework for patients without severe vascular disease

BackgroundNon-contrast chest CT is widely used for lung cancer screening, and its images carry potential information of the thoracic aorta. The morphological assessment of the thoracic aorta may have potential value in the presymptomatic detection of thoracic aortic-related diseases and the risk prediction of future adverse events. However, due to low vasculature contrast in such images, visual assessment of aortic morphology is challenging and highly depends on physicians’ experience. PurposeThe main objective of this study is to propose a novel multi-task framework based on deep learning for simultaneous aortic segmentation and localization of key landmarks on unenhanced chest CT. The secondary objective is to use the algorithm to measure quantitative features of thoracic aorta morphology. MethodsThe proposed network is composed of two subnets to carry out segmentation and landmark detection, respectively. The segmentation subnet aims to demarcate the aortic sinuses of the Valsalva, aortic trunk and aortic branches, whereas the detection subnet is devised to locate five landmarks on the aorta to facilitate morphology measures. The networks share a common encoder and run decoders in parallel, taking full advantage of the synergy of the segmentation and landmark detection tasks. Furthermore, the volume of interest (VOI) module and the squeeze-and-excitation (SE) block with attention mechanisms are incorporated to further boost the capability of feature learning. ResultsBenefiting from the multitask framework, we achieved a mean Dice score of 0.95, average symmetric surface distance of 0.53 mm, Hausdorff distance of 2.13 mm for aortic segmentation, and mean square error (MSE) of 3.23 mm for landmark localization in 40 testing cases. ConclusionWe proposed a multitask learning framework which can perform segmentation of the thoracic aorta and localization of landmarks simultaneously and achieved good results. It can support quantitative measurement of aortic morphology for further analysis of aortic diseases, such as hypertension.

Stable minimal hypersurfaces in ℝ^N+1+ℓ with singular set an arbitrary closed K⊂{0}×ℝ^ℓ

With respect to a $C^\infty$ metric which is close to the standard Euclidean metric on $\mathbb{R}^{N+1+\ell}$, where $N\ge 7$ and $\ell\ge 1$ are given, we construct a class of embedded $(N+\ell)$-dimensional hypersurfaces (without boundary) which are minimal and strictly stable, and which have singular set equal to an arbitrary preassigned closed subset $K\subset \{0\}\times \mathbb{R}^{\ell}$. Thus the question is settled, with a strong affirmative, as to whether there can be " gaps" or even fractional dimensional parts in the singular set. Such questions, for both stable and unstable minimal submanifolds, remain open in all dimensions in the case of real analytic metrics and, in particular, for the standard Euclidean metric. The construction used here involves the analysis of solutions $u$ of the symmetric minimal surface equation on domains $\Omega\subset\mathbb{R}^{n}$ whose symmetric graphs (i.e., $\{(x,\xi)\in\Omega\times \mathbb{R}^{m}: |\xi| = u(x)\}$) lie on one side of a cylindrical minimal cone including, in particular, a Liouville type theorem for complete solutions (i.e., the case $\Omega=\mathbb{R}^{n}$).

A mechanism-based multisurface plasticity model for hexagonal close-packed materials with detailed validation and assessment

We present a multi-surface model (MSM) of plasticity to simulate the anisotropic and asymmetric yield responses often observed in hexagonal close-packed (HCP) materials such as magnesium and its alloys. Motivated by the salient outcomes from high-resolution polycrystal plasticity simulations and experimental insights on magnesium alloys, we propose a three-surface representation incorporating two separate anisotropic, symmetric yield surfaces for the soft (basal) and hard (non-basal) glide, and an anisotropic, asymmetric yield surface for extension twinning. This reduced representation of the high-resolution crystal plasticity offers computational attractiveness to analyze large-scale engineering simulations while maintaining key micromechanical details of soft versus hard glide. The model is calibrated to a particular three-dimensional dataset for polycrystal magnesium alloy and is validated against a range of crystal plasticity results for different loading conditions. The capability of the model is then assessed for complex boundary-value problems at the macroscale (round notched bars) and at the microscale (void growth and coalescence). The validation and assessment demonstrate the ability of the proposed MSM to capture finer details of deformation such as identifying regions where soft and hard glide may occur around the void or in other heterogeneous loading states.

A medical unsupervised domain adaptation framework based on Fourier transform image translation and multi-model ensemble self-training strategy.

Well-established segmentation models will suffer performance degradation when deployed on data with heterogeneous features, especially in the field of medical image analysis. Although researchers have proposed many approaches to address this problem in recent years, most of them are feature-adaptation-based adversarial networks, the problems such as training instability often arise in adversarial training. To ameliorate this challenge and improve the robustness of processing data with different distributions, we propose a novel unsupervised domain adaptation framework for cross-domain medical image segmentation. In our proposed approach, Fourier transform guided images translation and multi-model ensemble self-training are integrated into a unified framework. First, after Fourier transform, the amplitude spectrum of source image is replaced with that of target image, and reconstructed by the inverse Fourier transform. Second, we augment target dataset with the synthetic cross-domain images, performing supervised learning using the original source set labels while implementing regularization by entropy minimization on predictions of unlabeled target data. We employ several segmentation networks with different hyperparameters simultaneously, pseudo-labels are generated by averaging their outputs and comparing to confidence threshold, and gradually optimize the quality of pseudo-labels through multiple rounds self-training. We employed our framework to two liver CT datasets for bidirectional adaptation experiments. In both experiments, compared to the segmentation network without domain alignment, dice similarity coefficient (DSC) increased by nearly 34% and average symmetric surface distance (ASSD) decreased by about 10. The DSC values were also improved by 10.8% and 6.7%, respectively, compared to the existing model. We propose a Fourier transform-based UDA framework, the experimental results and comparisons demonstrate that the proposed method can effectively diminish the performance degradation caused by domain shift and performs best on the cross-domain segmentation tasks. Our proposed multi-model ensemble training strategy can also improve the robustness of the segmentation system.

Three-point bending performances of integral-forming aluminum foam sandwich

In the course of service, integral-forming aluminum foam sandwich (IFAFS) needed to bear three-point bending loads in different directions, however, its deformation mechanism and failure modes were still unclear. In this work, three-point bending performances of IFAFS under flatwise and edgewise bending conditions were investigated by experiment, in-situ micro X-ray tomography and digital volume correlation (DVC) calculation. The results showed that three-point bending performance was more stable under edgewise bending condition, and with the decrease of span length IFAFS presented three different failure modes of oblique core shear, asymmetric and symmetrical surface fracture. In addition, porosity mutation was a significant reason for crack initiation, and optimizing pore homogeneity was important to improve the performance and predictability of failure location. Different strengthening effect of sandwich structure anisotropy and different internal deformation evolutions caused by internal strain vortex were two main reasons which lead to performance difference of IFAFS with different solid panel directions. Connection of pre-existing micropores with dimple-like micropores generated during deformation process leads to the failure of IFAFS. The key factors to further optimize three-point bending performance and predictability of IFAFS was to homogenize the pore distribution of IFAFS.

A simple single-cycle interactive strategy to improve deep learning-based segmentation of organs-at-risk in head-and-neck cancer.

Interactive segmentation seeks to incorporate human knowledge into segmentation models and thereby reducing the total amount of editing of auto-segmentations. By performing only interactions which provide new information, segmentation performance may increase cost-effectively. The aim of this study was to develop, evaluate and test feasibility of a deep learning-based single-cycle interactive segmentation model with the input being computer tomography (CT) and a small amount of information rich contours. A single-cycle interactive segmentation model, which took CT and the most cranial and caudal contour slices for each of 16 organs-at-risk for head-and-neck cancer as input, was developed. A CT-only model served as control. The models were evaluated with Dice similarity coefficient, Hausdorff Distance 95th percentile and average symmetric surface distance. A subset of 8 organs-at-risk were selected for a feasibility test. In this, a designated radiation oncologist used both single-cycle interactive segmentation and atlas-based auto-contouring for three cases. Contouring time and added path length were recorded. The medians of Dice coefficients increased with single-cycle interactive segmentation in the range of 0.004 (Brain)-0.90 (EyeBack_merged) when compared to CT-only. In the feasibility test, contouring time and added path length were reduced for all three cases as compared to editing atlas-based auto-segmentations. Single-cycle interactive segmentation improved segmentation metrics when compared to the CT-only model and was clinically feasible from a technical and usability point of view. The study suggests that it may be cost-effective to add a small amount of contouring input to deep learning-based segmentation models.

State constrained control strategy for unmanned surface vehicle trajectory tracking based on improved barrier Lyapunov function

This paper addresses the state-constrained control of the symmetric underactuated unmanned surface vehicles (USVs) under unknown dynamics and time-varying interference based on improved Lyapunov function. Inspired by the prescribed performance control (PPC), the improved logarithmic barrier Lyapunov function (BLF) is proposed with a reasonably defined performance function in advance. The tracking errors of position and yaw direction are constrained within the set range. Meanwhile, the symmetric barrier Lyapunov function (SBLF) and dynamic surface control (DSC) are introduced to simplify the controller design and indirectly restrict the yaw velocity. On this basis, the finite-time disturbance observer (FDO) is proposed to estimate the unknown disturbance. The finite-time auxiliary dynamic system is introduced to deal with the input saturation. In addition, the neural network minimum learning parameter (MLP) can reduce the computational complexity while figuring out the unknown dynamics. Finally, numerical simulation verifies the effectiveness of the control system.

Effect of Surface Nonlinearity Distribution on Second Harmonic Generation under Tightly Focused Beams

A thorough integral formulation describing the second harmonic generation under tightly focused linearly and radially polarized beams for an arbitrary distribution of the nonlinear susceptibility over the surface of isotropic media was presented. We numerically simulated effects caused by the straight edge of the nonlinear region. In particular, we demonstrated that the second harmonic radiation in the normal direction, which is forbidden in common cases of highly symmetrical nonlinear surfaces, is allowed in the presence of the edge. This is provided by χ(2)xzx-component of the second-order susceptibility.

Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs

Background and ObjectivesBedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs. MethodsTo improve the efficiency of the segmentation process, we introduced a "human-in-the-loop" segmentation workflow with an active learning approach, looking at five major anatomical structures in the chest (heart, lungs, mediastinum, trachea, and clavicles). This allowed us to decrease the time needed for segmentation by 32% and select the most complex cases to utilize human expert annotators efficiently. After annotation of 2,000 CXRs from different Level 1 medical centers at Charité – University Hospital Berlin, there was no relevant improvement in model performance, and the annotation process was stopped. A 5-layer U-ResNet was trained for 150 epochs using a combined soft Dice similarity coefficient (DSC) and cross-entropy as a loss function. DSC, Jaccard index (JI), Hausdorff distance (HD) in mm, and average symmetric surface distance (ASSD) in mm were used to assess model performance. External validation was performed using an independent external test dataset from Aachen University Hospital (n = 20). ResultsThe final training, validation, and testing dataset consisted of 1900/50/50 segmentation masks for each anatomical structure. Our model achieved a mean DSC/JI/HD/ASSD of 0.93/0.88/32.1/5.8 for the lung, 0.92/0.86/21.65/4.85 for the mediastinum, 0.91/0.84/11.83/1.35 for the clavicles, 0.9/0.85/9.6/2.19 for the trachea, and 0.88/0.8/31.74/8.73 for the heart. Validation using the external dataset showed an overall robust performance of our algorithm. ConclusionsUsing an efficient computer-aided segmentation method with active learning, our anatomy-based model achieves comparable performance to state-of-the-art approaches. Instead of only segmenting the non-overlapping portions of the organs, as previous studies did, a closer approximation to actual anatomy is achieved by segmenting along the natural anatomical borders. This novel anatomy approach could be useful for developing pathology models for accurate and quantifiable diagnosis.

2D reciprocal space map of etched metalorganic chemical vapor deposited CdTe(001) film surface on miscut GaAs(001)

CdTe thin films have drawn continued interest in research and applications in various fields such as optoelectronics. We report on the study of a two-dimensional (2D) reciprocal space map of Br-methanol etched CdTe(001) epitaxial film grown by metalorganic chemical vapor deposition on miscut GaAs(001) using azimuthal reflection high-energy electron diffraction (ARHEED), which reveals a two-fold symmetric surface with a rectangular unit mesh. This is in contrast to the observed four-fold symmetry with a square unit mesh on an etched miscut single crystal CdTe(001). Two sets of two-fold symmetric diffraction spots with unequal intensities indicate unequal populations of two 90° orientation domains in the etched film. Based on the 2D maps and tilted azimuthal dependent RHEED patterns of the etched film, it unveils that the short length of rectangular unit mesh in the dominant domain aligns with the step edge direction. A simple method to characterize the miscut angle of a heteroepitaxial film grown on a miscut substrate is demonstrated using ARHEED. Both etched single crystal and film surfaces are Te rich with a higher Te to Cd ratio on the elongated hillocks in the etched film disclosed by scanning Auger electron spectroscopy mapping. This methodology can be applied to examine etched epitaxial film's surfaces that might deviate from bulk structure or bulk stoichiometry before interfacing with other materials to form heterojunctions or making ohmic contacts with metals.

Microscopic description of α, 2α , and cluster decays of Rn216−220 and Ra220−224

$\ensuremath{\alpha}$ and cluster decays are analyzed for heavy nuclei located above $^{208}\mathrm{Pb}$ on the chart of nuclides: $^{216--220}\mathrm{Rn}$ and $^{220--224}\mathrm{Ra}$, which are also candidates for observing the $2\ensuremath{\alpha}$ decay mode. A microscopic theoretical approach based on relativistic energy density functionals (EDF), is used to compute axially symmetric deformation-energy surfaces as functions of quadrupole, octupole, and hexadecupole collective coordinates. Dynamical least-action paths for specific decay modes are calculated on the corresponding potential-energy surfaces. The effective collective inertia is determined using the perturbative cranking approximation, and zero-point and rotational energy corrections are included in the model. The predicted half-lives for $\ensuremath{\alpha}$ decay are within one order of magnitude of the experimental values. In the case of single-$\ensuremath{\alpha}$ emission, the nuclei considered in the present study exhibit least-action paths that differ significantly up to the scission point. The differences in $\ensuremath{\alpha}$-decay lifetimes are not only driven by $Q$ values, but also by variances of the least-action paths prior to scission. In contrast, the $2\ensuremath{\alpha}$ decay mode presents very similar paths from equilibrium to scission, and the differences in lifetimes are mainly driven by the corresponding $Q$ values. The predicted $^{14}\mathrm{C}$ cluster decay half-lives are within three orders of magnitudes of the empirical values, and point to a much more complex pattern compared with the $\ensuremath{\alpha}$-decay mode.

General form of the tunneling barrier for nanometrically sharp electron emitters

Field electron emission from nanometer-scale objects deviates from the predictions of the classical emission theory as both the electrostatic potential curves within the tunneling region and the image potential deviates from the planar one. This impels the inclusion of additional correction terms in the potential barrier. At the apex of a tip-like rotationally symmetric surface, these terms are proportional to the (single) local emitter curvature. The present paper generalizes this relation, showing that for any emitter geometry, the coefficient of the correction terms is given by the mean curvature, i.e., the average of the two principal curvatures.