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1. A measure model for the spread of viral infections with mutations

   https://doi.org/10.3934/nhm.2022015  

   Gong, Xiaoqian; Piccoli, Benedetto (January 2022, Networks and Heterogeneous Media)

   Genetic variations in the COVID-19 virus are one of the main causes of the COVID-19 pandemic outbreak in 2020 and 2021. In this article, we aim to introduce a new type of model, a system coupled with ordinary differential equations (ODEs) and measure differential equation (MDE), stemming from the classical SIR model for the variants distribution. Specifically, we model the evolution of susceptible \begin{document}$ S $$\end{document} and removed \begin{document}$$ R $$\end{document} populations by ODEs and the infected \begin{document}$$ I $$\end{document} population by a MDE comprised of a probability vector field (PVF) and a source term. In addition, the ODEs for \begin{document}$$ S $$\end{document} and \begin{document}$$ R $$\end{document} contains terms that are related to the measure \begin{document}$$ I $$\end{document}$. We establish analytically the well-posedness of the coupled ODE-MDE system by using generalized Wasserstein distance. We give two examples to show that the proposed ODE-MDE model coincides with the classical SIR model in case of constant or time-dependent parameters as special cases. 

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2. MHD Turbulent Power Anisotropy in the Inner Heliosphere

   https://doi.org/10.3847/1538-4357/ac70cb  

   Adhikari, L.; Zank, G. P.; Zhao, L.-L.; Telloni, D. (July 2022, The Astrophysical Journal)

   Abstract We study anisotropic magnetohydrodynamic (MHD) turbulence in the slow solar wind measured by Parker Solar Probe (PSP) and Solar Orbiter (SolO) during its first orbit from the perspective of variance anisotropy and correlation anisotropy. We use the Belcher & Davis approach (M1) and a new method (M2) that decomposes a fluctuating vector into parallel and perpendicular fluctuating vectors. M1 and M2 calculate the transverse and parallel turbulence components relative to the mean magnetic field direction. The parallel turbulence component is regarded as compressible turbulence, and the transverse turbulence component as incompressible turbulence, which can be either Alfvénic or 2D. The transverse turbulence energy is calculated from M1 and M2, and the transverse correlation length from M2. We obtain the 2D and slab turbulence energy and the corresponding correlation lengths from those transverse turbulence components that satisfy an angle between the mean solar wind flow speed and mean magnetic field θ UB of either (i) 65° < θ UB < 115° or (ii) 0° < θ UB < 25° (155° < θ UB < 180°), respectively. We find that the 2D turbulence component is not typically observed by PSP near perihelion, but the 2D component dominates turbulence in the inner heliosphere. We compare the detailed theoretical results of a nearly incompressible MHD turbulence transport model with the observed results of PSP and SolO measurements, finding good agreement between them. 

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3. Machine Learning-Assisted Near-Infrared Spectral Fingerprinting for Macrophage Phenotyping

   https://doi.org/10.1021/acsnano.4c03387  

   Nadeem, Aceer; Lyons, Sarah; Kindopp, Aidan; Jamieson, Amanda; Roxbury, Daniel (August 2024, ACS Nano)

   Spectral fingerprinting has emerged as a powerful tool that is adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coupled with machine learning techniques, we uncover complex interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells, enabling in situ phenotype discrimination. Utilizing Raman microscopy, we showcase statistically higher DNA-SWCNT uptake and a significantly lower defect ratio in M1 macrophages compared to M2 and naive phenotypes. NIR fluorescence data also indicate that distinctive intraendosomal environments of these cell types give rise to significant differences in many optical features, such as emission peak intensities, center wavelengths, and peak intensity ratios. Such features serve as distinctive markers for identifying different macrophage phenotypes. We further use a support vector machine (SVM) model trained on SWCNT fluorescence data to identify M1 and M2 macrophages, achieving an impressive accuracy of >95%. Finally, we observe that the stability of DNA-SWCNT complexes, influenced by DNA sequence length, is a crucial consideration for applications, such as cell phenotyping or mapping intraendosomal microenvironments using AI techniques. Our findings suggest that shorter DNA-sequences like GT6 give rise to more improved model accuracy (>87%) due to increased active interactions of SWCNTs with biomolecules in the endosomal microenvironment. Implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time monitoring of in vivo cellular differentiation. 

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4. Using cohesive zone models with digital image correlation to obtain a mixed mode I/II fracture envelope of a tough epoxy

   https://doi.org/10.1016/j.engfracmech.2023.109732  

   Jackson, Christopher M; McGuire, Jeffrey A; Losada, Martin E; Maskery, Ian; Ashcroft, Ian; De_Vita, Raffaella; Dillard, David A (January 2024, Engineering Fracture Mechanics)

   This work describes a method in which the digital image correlation (DIC) method and finite element analysis (FEA) were used to create a quasi-static mixed-mode fracture envelope for bonded joints consisting of 2024-T3 Al adherends and a tough structural epoxy adhesive. Symmetric and asymmetric versions of double cantilever beam, single-leg bend, and end-notched flexure tests are used to populate the mixed-mode fracture envelope with results at several mode mixities. Experiments are conducted in a universal testing machine while recording images for subsequent DIC analysis. Finite element analysis is used to implement cohesive zone models (CZMs) of the adhesive fracture and to account for plastic deformation of adherends. Mode I and mode II traction separation laws (TSLs) are determined from a property identification method with a Benzeggagh–Kenane mixed-mode coupling law used to model mixed-mode behavior. FEA results are shown to provide a good agreement to both the crosshead displacement and DIC data. The methods in this paper serve as a potential framework for future calibration of mixed-mode fracture envelopes for joints bonded with very tough adhesives that complicate assessment with traditional data analysis methods. 

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5. Multi-Pedestrian Safety Warning at Urban Intersections: Use Case of Digital Twin

   Fu, Y; Gao, Q; Ghasemi, M; Zussman, G; Di, X (May 2026, arXiv, in Proc. IEEE 29th Int. Conf. Intelligent Transportation Systems (ITSC), 2026.)

   Digital twins (DTs) for urban transportation systems have gained increasing attention; however, their systematic evaluation in safety-critical scenarios remains limited. This paper presents a multi-pedestrian safety warning system at urban intersections enabled by a tightly coupled physical–digital twin framework. Built upon the COSMOS city-scale wireless testbed in New York City, the proposed system integrates camera and ultra-wideband (UWB), edge-cloud computing, predictive trajectory modeling, and MQTT-based communication to deliver realtime safety alerts to vulnerable road users (VRUs). The system is evaluated through both field deployment and virtual reality (VR) experiments. Results demonstrate high warning generation accuracy, localization accuracy, efficient end-to-end latency under different model configurations, and significant reductions in user response time when warnings are issued. The proposed DT framework provides a scalable, modular, and generalizable solution for real-time multi-p 

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