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Semi-supervised clustering problems focus on clustering data with labels. In this paper,we consider the semi-supervised hypergraph problems. We use the hypergraph related tensor to construct an orthogonal constrained optimization model. The optimization problem is solved by a retraction method, which employs the polar decomposition to map the gradient direction in the tangent space to the Stefiel manifold. A nonmonotone curvilinear search is implemented to guarantee reduction in the objective function value. Convergence analysis demonstrates that the first order optimality condition is satisfied at the accumulation point. Experiments on synthetic hypergraph and hypergraph given by real data demonstrate the effectivity of our method.

Gold nanospheres (Au NSs) and gold nanorods (Au NRs) are traditional noble metal plasmonic nanomaterials. Particularly, Au NRs with tunable longitudinal plasmon resonance from visible to the near infrared (NIR) range were suitable for high efficient photothermal applications due to extended light receiving range. In this work, we synthesized Au NRs and Au NSs of similar volume, and subsequently developed them into Au NR/PVDF and Au NS/PVDF nanofilm, both of which exhibited excellent solar photothermal performance evaluated by solar photothermal experiments. We found that Au NR/PVDF nanofilm showed higher solar photothermal performance than Au NS/PVDF nanofilm. Through detailed analysis, such as morphological characterization, optical measurement, and finite element method (FEM) modeling, we found that the plasmonic coupling effects inside the aggregated Au NRs nanoclusters contributed to the spectral blue-shifts and intensified photothermal performance. As compared to Au NS/PVDF nanofilms, Au NR/PVDF nanofilm exhibited higher efficient light-to-heat conversion rate, because of the extended light receiving range and high absorbance, as the result of strong plasmonic interactions inside nanoclusters, which was further validated by monochromatic laser photothermal experiments and FEM simulations. Our work proved that the Au NRs have huge potential for plasmonic solar photothermal applications, and are envisioned for novel plasmonic applications.

Pan-microbial inactivation technologies that do not require high temperatures, reactive chemical compounds, or UV radiation could address gaps in current infection control strategies and provide efficient sterilization of biologics in the biotechnological industry. Here, we demonstrate that femtosecond (fs) laser irradiation of resonant gold nanoparticles (NPs) under conditions that allow for E-field mediated cavitation and shockwave generation achieve an efficient plasmon-enhanced photonic microbial pathogen inactivation. We demonstrate that this NP-enhanced, physical inactivation approach is effective against a diverse group of pathogens, including both enveloped and non-enveloped viruses, and a variety of bacteria and mycoplasma. Photonic inactivation is wavelength-dependent and in the absence of plasmonic enhancement from NPs, negligible levels of microbial inactivation are observed in the near-infrared (NIR) at 800 nm. This changes upon addition of resonant plasmonic NPs, which provide a strong enhancement of inactivation of viral and bacterial contaminants. Importantly, the plasmon-enhanced 800 nm femtosecond (fs)-pulse induced inactivation was selective to pathogens. No measurable damage was observed for antibodies included as representative biologics under identical conditions.

The finite-difference time-domain (FDTD) method has been commonly utilized to simulate the electromagnetic (EM) waves propagation in the plasma media. However, the FDTD method may bring about extra run-time on concerning computationally large and complicated EM problems. Fortunately, the FDTD method is easy to parallelize. Besides, GPU has been widely used for parallel computing due to its unique SPMD (Single Program Multiple Data) architecture. In this paper, we represent the parallel Runge-Kutta exponential time differencing scheme FDTD (RKETD) method for the unmagnetized plasma implemented on GPU. The detailed flowchart of parallel RKETD-FDTD method is described. The accuracy and acceleration performance of the proposed parallel RKETD-FDTD method implemented on GPU are substantiated by calculating the reflection and transmission coefficients for one-dimensional unmagnetized plasma slab. The results indicate that the numerical precision of the parallel RKETD-FDTD scheme is consistent with that of the code implemented on CPU. The computation efficiency is greatly improved compared with merely CPU-based serial RKETD-FDTD method. Moreover, the comparisons of the performance of CUDA-based GPU parallel program, OpenMP (Open Multi-Processing)-based CPU parallel program, and single-CPU serial program on the same host computer are done. Compared with the serial program, both parallel programs get good results, while GPU-based parallel program gains better result., Comment: 12 pages,3 figures