Silver Nanoparticles Decorated with PEGylated Porphyrins as Potential Theranostic and Sensing Agents

Silver nanoparticles (AgNPs) stand out over other metal nanoparticles thanks to their peculiar bactericidal and spectroscopic properties. Tunability of the AgNPs chemical–physical properties could be provided through their organic covalent coating. On the other hand, PEGylated porphyrin derivatives are versatile heteromacrocycles investigated for uses in the biomedical field as cytotoxic and tracking agents, but also as sensors. In this work, an easy multi-step approach was employed to produce coated silver nanoparticles. Specifically, the AgNPs were functionalized with 5,10,15-[p-(ω-methoxy-polyethyleneoxy)phenyl]-20-(p-hydroxyphenyl)-porphyrin (P(PEG350)3), using chloropropanethiol as a coupling agent. The P(PEG350)3 was structurally characterized through MALDI-TOF mass spectrometry, NMR spectroscopy and thermal analyses. The functionalization of AgNPs was monitored step-by-step employing UV-Vis spectroscopy, dynamic light scattering and thermogravimetric techniques. HRTEM and STEM measurements were used to investigate the morphology and the composition of the resulting nanostructured system (AgNP@P(PEG350)3), observing a long-range alignment of the outer porphyrin layer. The AgNP@P(PEG350)3 combines the features of the P(PEG350)3 with those of AgNPs, producing a potential multifunctional theranostic tool. The nanosystem revealed itself suitable as a removable pH sensor in aqueous solutions and potentially feasible for biological environment applications.

W-Band Multi-Aspect High Resolution Range Profile Radar Target Classification Using Support Vector Machines

Millimeter-wave (W-band) radar measurements were taken for two maritime targets instrumented with attitude and heading reference systems (AHRSs) in a littoral environment with the aim of developing a multiaspect classifier. The focus was on resource-limited implementations such as short-range, tactical, unmanned aircraft systems (UASs) and dealing with limited and imbalanced datasets. Radar imaging and preprocessing consisted of recording high-resolution range profiles (HRRPs) and performing range alignment using peak detection and fast Fourier transforms (FFTs). HRRPs were used because of their simplicity, reliability, and speed. The features used were fixed-length, frequency domain range profiles. Two linear support vector machine (SVM)-based classifiers were developed which both yielded excellent results in their general forms and were simple to implement. The first approach utilized the positive predictive value (PPV) and negative predictive value (NPV) statistics of the SVM directly to generate target probabilities and consequently determine the optimal aspect transitions for classification. The second approach used the Kolmogorov–Smirnov test for dimensionality reduction, followed by concatenating feature vectors across several aspects. The latter approach is particularly well-suited to resource-constrained scenarios, potentially allowing for retraining and updating in the field.

Global regularity for a 1D Euler-alignment system with misalignment

We study one-dimensional Eulerian dynamics with nonlocal alignment interactions, featuring strong short-range alignment, and long-range misalignment. Compared with the well-studied Euler-alignment system, the presence of the misalignment brings different behaviors of the solutions, including the possible creation of vacuum at infinite time, which destabilizes the solutions. We show that with a strongly singular short-range alignment interaction, the solution is globally regular, despite the effect of misalignment.

Tailored growth of graphene oxide liquid crystals with controlled polymer crystallization in GO-polymer composites

Kinetics of polymer crystallization determines the liquid crystallinity of graphene oxide and its long range alignment.

Noise-Robust Range Alignment Method for Inverse Synthetic Aperture Radar Based on Aperture Segmentation and Average Range Profile Correlation

Range alignment is an essential procedure in the translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum-correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novelanti-noise range alignment approach is proposed. In this new method, the target motion is modelled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once the range profiles of eachsub-aperture are aligned, the non-coherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture because the former step focuses mainly on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.

Noise-Robust Range Alignment Method for Inverse Synthetic Aperture Radar Based on Aperture Segmentation and Average Range Profiles Correlation

Range alignment is an essential procedure in translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novel anti-noise range alignment approach is proposed. In this new method, the target's motion is modelled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once each sub-aperture's range profiles are aligned, the noncoherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture for the reason that the former step mainly focuses on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.