Leaky waves propagating around the circumference or along the axis of immersed or buried cylindrical waveguides play a prominent role in the guided wave non-destructive evaluation inspection of submerged or embedded structures; a thorough understanding of the complex characteristics of leaky waves in such systems is essential for a range of applications. In this contribution, we apply a spectral collocation approach for the computation of the dispersion curves of both leaky circumferential and axial waves radiating from submerged or embedded multilayered cylinders. Therein, a complex coordinate transformation of the exterior ensures the numerical decay of the wave field of interest, while also preserving all of its physical characteristics. The proposed discretization enables the dispersion curves of leaky waves to be retrieved as eigenvalues of a simple polynomial eigenvalue problem and avoids the numerical instabilities associated with Bessel functions or the non-linear eigenvalue problems one often encounters in such calculations. Results were validated against both analytical methods and dispersion curves from commercially available root-finding software.

Weakly textured closed pipelines-including energy and buried drainage conduits-require timely inspection of cracks, corrosion, joints and related defects. Weak illumination, sparse texture and repetitive cylindrical geometry degrade visual odometry and RGB-D SLAM, especially along the pipe axis, while image-level detectors rarely provide the axial distance, circumferential angle and pipe-frame 3D coordinates needed for maintenance. This paper presents the Structure-Constrained Pipe Joint Estimator (SC-PipeJE), an RGB-D framework that jointly estimates robot poses and 3D damage locations under weak texture. SC-PipeJE improves YOLOv8-seg for structural landmarks and damage masks, fits cylinders and centerlines from local point clouds, and optimizes a sliding window that couples RGB-D odometry with continuous geometric residuals, discrete landmarks and multi-frame damage factors so that damage observations also refine pose. On a primary RGB-D corpus of 5468 annotated frames and multi-structure sequences, the detector reaches mAP@0.5:0.95 of 0.7194 and Mask AP of 0.6980. Absolute trajectory error is 0.0343 m (67.6% lower than RTAB-Map under the same RGB-D protocol), and multi-frame damage localization yields 32.88 mm mean 3D error (46.6% lower than single-frame back-projection) at about 20 FPS. Quantitative pose and 3D damage results are reported on the laboratory RGB-D Corpus A; a complementary CCTV subset (Corpus B) is used only for qualitative appearance stress checks and is not mixed into the quantitative protocol. Ablation and robustness studies show that continuous geometry, discrete landmarks and multi-frame damage factors provide complementary observability when appearance cues are unreliable.

Large-scale metal-lined composite overwrapped pressure vessels are promising for cryogenic liquid oxygen storage, but their response is governed by the interaction among dome geometry, thermal mismatch and internal pressure. This study investigates the influence of dome aspect ratio (DAR), defined as the ratio of the major to minor semi-axis of an ellipsoidal dome, on vessel thermomechanical behavior. Six DAR values from 1.25 to 2.50 were analyzed using a three-dimensional finite element model with temperature-dependent properties of Type 304 stainless steel and T700-grade carbon/epoxy. Three loading conditions were considered including 1.0 MPa internal pressure at 293 K, cooling from 293 to 90 K, and pressurization at 90 K. At 293 K, increasing DAR intensified stress localization near the dome-cylinder transition. Cryogenic cooling produced incompatible free deformations between the liner and overwrap, generating substantial constrained thermal stresses after bonding. Subsequent pressurization further increased the liner stress but partly offset the cooling-induced compressive stress in the overwrap. Under cryogenic and combined loading, DAR had little influence on the central cylindrical section, but markedly changed the stress magnitude, sign and localization in the domes and transition regions. These findings indicate that dome shapes for cryogenic vessels should not be selected solely from room-temperature pressure-based conclusions, because thermal mismatch and cryogenic pressurization can fundamentally alter the structural response.

Objectives: To evaluate a general-purpose large language model (LLM)'s ability to perform surgeon-level decision-making in choosing an intraocular lens (IOL) power when provided the same preoperative data. Methods: This study included 293 eyes (174 patients) that underwent monofocal or toric cataract surgery performed by a single surgeon. GPT-5.2 analyzed the same biometric printouts, topographic displays, and preoperative data used in surgical planning. Postoperative refraction for GPT-5.2-selected IOLs was simulated from the measured postoperative refraction using a fixed conversion factor. The mean refractive deviation from target, mean absolute error (MAE), and number of eyes within ±0.25 D and ±0.50 D of target refraction were calculated. A subgroup analysis evaluated toric IOL cylinder magnitude and axis. Results: GPT-5.2-selected IOL power differed significantly from that of the surgeon (19.24 ± 4.01 vs. 19.64 ± 4.07 D [mean ± SD], p < 0.001). Using simulated refractive outcomes for GPT-5.2, MAE was higher than for surgeon-selected IOLs (0.45 ± 0.03 vs. 0.35 ± 0.02 D [mean ± SE], p = 0.002), and a greater percentage of eyes fell within ±0.25 D of target refraction for the surgeon (51.9% vs. 44.7%, p < 0.01), with no difference within ±0.50 D (p = 0.06). In toric eyes, the cylinder magnitude did not differ, but simulated residual astigmatism was higher for GPT-5.2 (0.78 ± 0.55 vs. 0.58 ± 0.47 D, p < 0.001), with an axis flip in 47.1% versus 39.2% of eyes. Conclusions: GPT-5.2 synthesized clinical data and approximated surgeon IOL selection, but its simulated refractive outcomes were less accurate than the surgeon's measured outcomes. These findings suggest that a domain-specific LLM may serve as a support tool in IOL selection and cataract surgery planning.

We describe a contactless heat engine in which quantum and thermal electromagnetic fluctuations act as the working medium. The setup consists of two concentric cylinders held at different temperatures. The inner cylinder stably levitates within the outer one due to repulsive nonequilibrium Casimir forces. The chirality of the setup is broken by using nonreciprocal dielectric materials, akin to application of a magnetic field along the common cylinder axis. Using Rytov fluctuational electrodynamics, we show that heat transfer and torque can be expressed in terms of an angular-momentum-resolved heat flux density, Φn(ω): each exchanged photon carries energy ℏω and angular momentum ℏn. In reciprocal media, contributions from modes n and -n cancel and there is no net torque; nonreciprocity breaks this symmetry and powers rotation of the inner cylinder. Even in the absence of contact, electromagnetic fluctuations produce a frictional torque opposing rotation that we compute. This enables computation of characteristic steady state rotations, and estimation of the engine efficiency (which remains bounded by the Carnot limit). The cylindrical setup provides a natural realization of fluctuation-induced angular-momentum transfer and a possible route toward nanoscale contactless engines.

The nozzle geometries associated with extrusion-based 3D printing impart significant spatial heterogeneities in both the type (shear vs extension) and rate of the flows applied during extrusion. For polymeric and nanostructured inks, these heterogeneous flow histories influence the underlying structure at the nano- to microscale. We previously demonstrated that HOT-DIW 3D printing can orient the nanostructure of a cylinder-forming block copolymer along a programmed print path, enabling tunable mechanical anisotropy. To probe the subfilament heterogeneities in nanostructure that arise in these oriented materials from the spatially varied flow histories, we apply scanning microbeam small-angle X-ray scattering (SM-SAXS). From this data, we identify a flow-rate-dependent emergence of off-axis tilted domains, which develop upon thermal annealing. We propose that application of shear followed by extension during 3D printing imparts asymmetric stretching of bridging chain conformations. Relaxation of this trapped stress upon thermal annealing imparts a torque on the shortened polystyrene domains which drives the formation of long-range ordered cylinders at an angle offset to the print direction. We show that the distribution of tilts within 3D-printed filaments is consistent with the variability in printed filament stiffness. Ultimately, these flow-structure insights highlight the need to understand how "real" mixed flows of complex fluids such as block copolymer melts drive the assembly, structure, and properties of printed materials.