Proton resonance frequency shift-based MR thermometry is a promising temperature monitoring approach for thermotherapy but its accuracy is vulnerable to inter-scan motion. Model-based referenceless thermometry has been proposed to address this problem but phase unwrapping is usually needed before the model fitting process. In this paper, a referenceless MR thermometry method using phase finite difference that avoids the time consuming phase unwrapping procedure is proposed. Unlike the previously proposed phase gradient technique, the use of finite difference in the new method reduces the fitting error resulting from the ringing artifacts associated with phase discontinuity in the calculation of the phase gradient image. The new method takes into account the values at the perimeter of the region of interest because of their direct relevance to the extrapolated baseline phase of the region of interest (where temperature increase takes place). In simulation study, in vivo and ex vivo experiments, the new method has a root-mean-square temperature error of 0.35 °C, 1.02 °C and 1.73 °C compared to 0.83 °C, 2.81 °C, and 3.76 °C from the phase gradient method, respectively. The method also demonstrated a slightly higher, albeit small, temperature accuracy than the original referenceless MR thermometry method. The proposed method is computationally efficient (~0.1 s per image), making it very suitable for the real time temperature monitoring.

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Structural health monitoring techniques based on vibration parameters have been used to assess the internal delamination damage of fiber-reinforced polymer composites. Recently, machine learning algorithms have been adopted to solve the inverse problem of predicting delamination parameters of the delamination from natural frequency shifts. In this article, a delamination detection methodology is proposed based on the changes in multiple modes of frequencies to assess the interface, location, and size of delamination in fiber-reinforced polymer composites. Three types of machine learning algorithms including back propagation neural network, extreme learning machine, and support vector machine algorithm were adopted as inverse algorithms for assessment of the delamination parameters, with a special focus on the interface prediction. A theoretical model of fiber-reinforced polymer beam with delamination under vibration was constructed to learn how the frequencies are affected by the delaminations (“forward problem”) and to generate a database of “frequency shifts versus delamination parameters” to be used in machine learning algorithms for delamination prediction (“inverse problem”). Multiple carbon/epoxy fiber-reinforced polymer beam specimens were manufactured and measured by a laser scanning Doppler vibrometer to extract the modal frequencies. Numerical and experimental verification results have shown that support vector machine has the best prediction performance among the three machine learning algorithms, with high prediction accuracy and only requiring a small number of samples. For predicting the interface of delamination which is a discrete variable, support vector machine classification has observed better prediction accuracy and requiring less running time than regression. This study is one of the first to prove the applicability of support vector machine for structural health monitoring of delamination damage in fiber-reinforced polymer composites and has the potential to improve the prediction capability of machine learning algorithms. Another significant outcome of the study is that the interface of delamination has been predicted accurately with support vector machine.

Metamaterial with hyperbolic dispersion properties can effectively manipulate plasmonic resonances. Here, we designed a hyperbolic metamaterial (HMM) substrate with a near-zero dielectric constant in the near-infrared region to manipulate the plasmon resonance of the nano-antenna (NA). For NA arrays, tuning the equivalent permittivity of HMM substrate by modifying the thickness of Au/diamond, the wavelength range of plasmon resonance can be manipulated. When the size of the NA changes within a certain range, the spectral position of the plasmon resonance will be fixed in a narrow band close to the epsilon-near-zero (ENZ) wavelength and produce a phenomenon similar to “pinning effect.” In addition, since the volume plasmon polaritons (VPP) mode is excited, it will couple with the localized surface plasmon (LSP) mode to generate a spectrum splitting. Therefore, the plasmon resonance is significantly affected and can be precisely controlled by designing the HMM substrate.

Dielectric materials exhibit negligible dissipative losses and strong electromagnetic multipolar optical responses at operating wavelengths compared to metallic materials. In a high-refractive-index dielectric, the destructive interference of the radiation fields from electric and toroidal dipole moments results in the anapole, which has the optical properties of a dark state and cannot emit energy in the far-field region. This study uses periodic Si nanocubes (SN) to support anapole excitation with lattice resonance based on numerical simulation and manipulates the anapole by tailoring structures according to the electromagnetic field distribution characteristics of the anapole. Long slits are introduced in the center to regulate the electric dipole moments, and elliptical holes are opened on both sides to adjust the current distribution and thus influence the toroidal dipole moments. The reflection resonance peak of tailored Si nanocubes shows a narrow bandwidth of 0.11 nm and a large Q-factor of 8053. Also, their reflection resonance peak shows more than 237 times the electric field enhancement in the dielectric. The tailored Si nanocubes provide an alternative to plasmonic metal to support hot spots and achieve near-field enhancement, which can be applied to enhance photon emission, surface-enhanced Raman scattering (SERS), and photocatalysis.

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