Tunable structural color has gained significant attention due to its dynamic characteristics. However, conventional devices are usually regulated only in their color capabilities by structural parameters, restricting real-time dynamic applications. In this study, we propose an ultra-thin asymmetric Fabry–Perot cavity patterned with phase-change materials (MPMP). The reversible phase transition of VO2 induces changes in the MPMP’s optical performance, enabling color mode switching through temperature control and resulting in rapid color conversion and low-temperature regulation. By adjusting relevant structural parameters of the VO2 layer and nanodiscs, the color performance range can be tailored. Through numerical investigations, we demonstrate that MPMP can produce stable transformation of dynamic structural colors by harnessing the phase-change effect. Our research unveils new possibilities for applications such as anti-counterfeiting, bio/chemical sensing, and temperature sensing.
This paper presents a structure for refractive index sensors in the terahertz (THz) band. The THZ sensor is studied in simulation, utilizing the strong local electromagnetic field intensity produced by the enhanced extraordinary optical transmission. Depending on the different sensing positions of the sensor, their sensing basis is also different, such as Mie scattering, surface plasmon polaritons, etc. The sensing sensitivity based on Mie scattering can reach 51.56 GHz/RIU; meanwhile the sensing sensitivity based on surface plasmon polaritons is only 5.13 GHz/RIU. The sensor can also detect the thickness of the analyte, with the lowest detectable height of 0.2 µm. Additionally, we find that the sensitivity can be increased by replacing the silicon particle with the analyte.
BackgroundDeep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated.PurposeTo implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences.Study TypeRetrospective.PopulationA total of 569 local cases as internal cohort (50.2 ± 11.2 years; 100% female), divided among training (218), validation (73) and testing (278); 125 cases from a public dataset as the external cohort (53.6 ± 11.5 years; 100% female).Field Strength/SequenceT1‐weighted imaging and dynamic contrast‐enhanced MRI (DCE‐MRI) with gradient echo sequences, T2‐weighted imaging (T2WI) with spin‐echo sequences, diffusion‐weighted imaging with single‐shot echo‐planar sequence and at 1.5‐T.AssessmentA convolutional neural network and long short‐term memory cascaded network was implemented for lesion classification with histopathology as the ground truth for malignant and benign categories and contralateral breasts as healthy category in internal/external cohorts. BI‐RADS categories were assessed by three independent radiologists as comparison, and class activation map was employed for lesion localization in internal cohort. The classification and localization performances were assessed with DCE‐MRI and non‐DCE sequences, respectively.Statistical TestsSensitivity, specificity, area under the curve (AUC), DeLong test, and Cohen's kappa for lesion classification. Sensitivity and mean squared error for localization. A P‐value <0.05 was considered statistically significant.ResultsWith the optimized mpMRI combinations, the lesion classification achieved an AUC = 0.98/0.91, sensitivity = 0.96/0.83 in the internal/external cohorts, respectively. Without DCE‐MRI, the DL‐based method was superior to radiologists' readings (AUC 0.96 vs. 0.90). The lesion localization achieved sensitivities of 0.97/0.93 with DCE‐MRI/T2WI alone, respectively.Data ConclusionThe DL method achieved high accuracy for lesion detection in the internal/external cohorts. The classification performance with a contrast agent‐free combination is comparable to DCE‐MRI alone and the radiologists' reading in AUC and sensitivity.Evidence Level3.Technical EfficacyStage 2.
Abstract-Ships are usually large-welded structures, and residual stress would inevitably occur in the welding process. At present, high-strength steel has been more and more widely used in ship structures, and it has high sensitivity to residual stress. At the same time, in the high temperature during the welding process, the hydrogen-containing compound in the arc welding is decomposed into monoatomic hydrogen, which is dissolved in the molten pool in a large amount. Uneven weld residual stresses in such structures can promote the diffusion and accumulation of hydrogen in the steel, resulting in excess hydrogen at the weld joint. This behavior can lead to hydrogen embrittlement, threatening the safety and reliability of the ship's structure. In this study, a three-dimensional finite element analysis thermodynamic model for the flat butt welding joints of high-strength steel was established, the welding process was simulated, and the distribution law for the welding residual stress field was obtained based on the thermal elastic-plastic theory. Then the sequential coupling calculation of hydrogen diffusion was performed by defining the welding residual stress field of flat butt welding joint of high strength steel as the pre-defined field, and the hydrogen diffusion behavior under the welding residual stress field was obtained based on the theory of residual stress-induced hydrogen diffusion. The results show that the welding residual stress level decreases rapidly with the increase of the weld distance. The welding residual stress affects the hydrogen diffusion behavior, hydrogen is enriched in the zone where the residual stress is high, and the heat affected zone is the region with high residual stress. These results could provide theoretical support for ensuring the safety and reliability of large ship structures.
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