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  1. Polypropylene (PP) and its composites are one of the hardest to directly join with metals due to their inherent chemical incompatibility. This paper presents a simple, efficient, and cost-effective method for joining PP composite to aluminum alloy in spot welding configuration by seeding the functional groups via an insert layer of PA6 thin film without requiring surface or material pre-treatment. The resulting joint loading capacity is shown to be sufficiently high to consistently develop failures in PP substrates in lap shear tensile tests away from the bonded area. Joint interface microstructure features are examined in detail. Bonding mechanisms are then described based on the detailed observations obtained in this study. 
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    Free, publicly-accessible full text available January 6, 2024
  2. Raman microscopy is a powerful analytical technique for materials and life sciences that enables mapping the spatial distribution of the chemical composition of a sample. State-of-the-art Raman microscopes, based on point-scanning frequency-domain detection, have long (∼1s) pixel dwell times, making it challenging to acquire images of a significant area (e.g., 100×100µm). Here we present a compact wide-field Raman microscope based on a time-domain Fourier-transform approach, which enables parallel acquisition of the Raman spectra on all pixels of a 2D detector. A common-path birefringent interferometer with exceptional delay stability and reproducibility can rapidly acquire Raman maps (∼30min for a 250000pixel image) with high spatial (<1µm) and spectral (∼23cm−1) resolutions. Time-domain detection allows us to disentangle fluorescence and Raman signals, which can both be measured separately. We validate the system by Raman imaging plastic microbeads and demonstrate its multimodal operation by capturing fluorescence and Raman maps of a multilayer-WSe2sample, providing complementary information on the strain and number of layers of the material.

     
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  3. ABSTRACT

    The project MOMO (Multiwavelength Observations and Modelling of OJ 287) was set up to test predictions of binary supermassive black hole (SMBH) scenarios and to understand disc–jet physics of the blazar OJ 287. After a correction, the precessing binary (PB) SMBH model predicted the next main outburst of OJ 287 in 2022 October, making the outburst well observable and the model testable. We have densely covered this period in our ongoing multifrequency radio, optical, ultraviolet (UV), and X-ray monitoring. The predicted outburst was not detected. Instead, OJ 287 was at low optical–UV emission levels, declining further into November. The predicted thermal bremsstrahlung spectrum was not observed either, at any epoch. Further, applying scaling relations, we estimate an SMBH mass of OJ 287 of 108 M⊙. The latest in a sequence of deep low states that recur every 1–2 yr is used to determine an upper limit on the Eddington ratio and on the accretion-disc luminosity. This limit is at least a factor of 10 lower than required by the PB model with its massive primary SMBH of >1010 M⊙. All these results favour alternative binary SMBH models of OJ 287 that require neither strong orbital precession nor a very large mass of the primary SMBH.

     
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  4. ABSTRACT Extremely elongated, conducting dust particles (also known as metallic ‘needles’ or ‘whiskers’) are seen in carbonaceous chondrites and in samples brought back from the Itokawa asteroid. Their formation in protostellar nebulae and subsequent injection into the interstellar medium have been demonstrated, both experimentally and theoretically. Metallic needles have been suggested to explain a wide variety of astrophysical phenomena, ranging from the mid-infrared interstellar extinction at $\sim \,$3–8$\, {\rm \mu m}$ to the thermalization of starlight to generate the cosmic microwave background. To validate (or invalidate) these suggestions, an accurate knowledge of the optics (e.g. the amplitude and the wavelength dependence of the absorption cross sections) of metallic needles is crucial. Here we calculate the absorption cross sections of iron needles of various aspect ratios over a wide wavelength range, by exploiting the discrete dipole approximation, the most powerful technique for rigorously calculating the optics of irregular or nonspherical grains. Our calculations support the earlier findings that the antenna theory and the Rayleigh approximation, which are often taken to approximate the optical properties of metallic needles, are indeed inapplicable. 
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  6. We propose a new Scaled Population (SP) based arithmetic computation approach that achieves considerable improvements over existing stochastic computing (SC) techniques. First, SP arithmetic introduces scaling operations that significantly reduce the numerical errors as compared to SC. Experiments show accuracy improvements of a single multiplication and addition operation by 6.3X and 4X, respectively. Secondly, SP arithmetic erases the inherent serialization associated with stochastic computing, thereby significantly improves the computational delays. We design each of the operations of SP arithmetic to take O(1) gate delays, and eliminate the need of serially iterating over the bits of the population vector. Our SP approach improves the area, delay and power compared with conventional stochastic computing on an FPGA-based implementation. We also apply our SP scheme on a handwritten digit recognition application (MNIST), improving the recognition accuracy by 32.79% compared to SC. 
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  7. Brain imaging genetics is an important research topic in brain science, which combines genetic variations and brain structures or functions to uncover the genetic basis of brain disorders. Imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary but different information. Unfortunately, we do not know the extent to which phenotypic variance is shared among multiple imaging modalities, which might trace back to the complex genetic mechanism. In this study, we propose a novel dirty multi-task SCCA to analyze imaging genetics problems with multiple modalities of brain imaging quantitative traits (QTs) involved. The proposed method can not only identify the shared SNPs and QTs across multiple modalities, but also identify the modality-specific SNPs and QTs, showing a flexible capability of discovering the complex multi-SNP-multi-QT associations. Compared with the multi-view SCCA and multi-task SCCA, our method shows better canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. This demonstrates that the proposed dirty multi-task SCCA could be a meaningful and powerful alternative method in multi-modal brain imaging genetics. 
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  8. This paper proposes a new meta-learning method – named HARMLESS (HAwkes Relational Meta LEarning method for Short Sequences) for learning heterogeneous point process models from short event sequence data along with a relational network. Specifically, we propose a hierarchical Bayesian mixture Hawkes process model, which naturally incorporates the relational information among sequences into point process modeling. Compared with existing methods, our model can capture the underlying mixed-community patterns of the relational network, which simultaneously encourages knowledge sharing among sequences and facilitates adaptive learning for each individual sequence. We further propose an efficient stochastic variational meta expectation maximization algorithm that can scale to large problems. Numerical experiments on both synthetic and real data show that HARMLESS outperforms existing methods in terms of predicting the future events. 
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