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Freshwater mussels are essential parts of our ecosystems to reduce water pollution. As natural bio-filters, they deactivate pollutants such as heavy metals, providing a sustainable method for water decontamination. This project will enable the use of Artificial Intelligence (AI) to monitor mussel behavior, particularly their gaping activity, to use them as bio-indicators for early detection of water contamination. In this paper, we employ advanced 3D reconstruction techniques to create detailed models of mussels to improve the accuracy of AI-based analysis. Specifically, we use a state-of-the-art 3D reconstruction tool, Neural Radiance Fields (NeRF), to create 3D models of mussel valve configurations and behavioral patterns. NeRF enables 3D reconstruction of scenes and objects from a sparse set of 2D images. To capture these images, we developed a data collection system capable of imaging mussels from multiple viewpoints. The system featured a turntable made of foam board with markers around the edges and a designated space in the center for mounting the mussels. The turntable was attached to a servo motor controlled by an ESP32 microcontroller. It rotated in a few degree increments, with the ESP32 camera capturing an image at each step. The images, along with degree information and timestamps, are stored on a Secure Digital (SD) memory card. Several components, such as the camera holder and turntable base, are 3D printed. These images are used to train a NeRF model using the Python-based Nerfstudio framework, and the resulting 3D models were viewed via the Nerfstudio API. The setup was designed to be user-friendly, making it easy for educational outreach engagements and to involve secondary education by replicating and operating 3D reconstructions of their chosen objects. We validated the accessibility and the impact of this platform in a STEM education summer program. A team of high school students from the Juntos Summer Academy at NC State University worked on this platform, gaining hands-on experience in embedded hardware development, basic machine learning principles, and 3D reconstruction from 2D images. We also report on their feedback on the activity. 

Sea turtles are one taxon of high conservation concern that encounter many pathogens, but their disease ecology is understudied, hindering our ability to predict impacts of disease on population viability. Fibropapillomatosis (FP) is a neoplastic tumor-forming disease that has been documented in all sea turtle species, with an especially high prevalence in green turtlesChelonia mydas.Here, we use Hawaiian green turtles (honu) as a study system to examine the roles of immunogenetic diversity and transcriptional modulation in sea turtle disease responses. Specifically, we quantified gene expression profiles associated with FP and characterized host diversity of major histocompatibility complex class I (MHCI) immune loci. We found 65 genes differentially expressed in blood between clinically healthy (n = 5) and FP-afflicted turtles (n = 5) with enriched biological processes of the innate immune system, aligned with expectations of reptilian immune systems and active disease resistance. Our results also suggest a role for disease tolerance in response to FP, as evidenced by enriched biological processes related to regulation of immune and metabolic homeostasis, increase in cellular detoxification, and increased tissue repair mechanisms. Honu (n = 89) had 23 unique MHCI alleles belonging to 3 distinct functional supertypes, but none were significantly associated with FP; this could be a result of intrinsic demographic properties of the population or reflect a lesser/differing role of the reptilian adaptive immune system. Our study advances the understanding of reptilian disease response and evolutionary mechanisms underlying immunogenetic diversity, both of which are important for promoting the adaptive potential of species vulnerable to extinction. 

Gaussian processes are widely employed as versatile modelling and predictive tools in spa- tial statistics, functional data analysis, computer modelling and diverse applications of machine learning. They have been widely studied over Euclidean spaces, where they are specified using covariance functions or covariograms for modelling complex dependencies. There is a growing literature on Gaussian processes over Riemannian manifolds in order to develop richer and more flexible inferential frameworks for non-Euclidean data. While numerical approximations through graph representations have been well studied for the Mat´ern covariogram and heat kernel, the behaviour of asymptotic inference on the param- eters of the covariogram has received relatively scant attention. We focus on asymptotic behaviour for Gaussian processes constructed over compact Riemannian manifolds. Build- ing upon a recently introduced Mat´ern covariogram on a compact Riemannian manifold, we employ formal notions and conditions for the equivalence of two Mat´ern Gaussian random measures on compact manifolds to derive the parameter that is identifiable, also known as the microergodic parameter, and formally establish the consistency of the maximum like- lihood estimate and the asymptotic optimality of the best linear unbiased predictor. The circle is studied as a specific example of compact Riemannian manifolds with numerical experiments to illustrate and corroborate the theory 

Abstract Two different types of monoclinic HfO 2 nanocrystals were employed in this work to study the effect of nanocrystal shape and crystallinity on the structural defects in the YBa2Cu3O7−δ (YBCO) matrix as it leads to an enhancement of pinning performances of solution-derived YBCO nanocomposite films. In this work the nanorod-like HfO 2 nanocrystals obtained from surfactant-controlled synthesis led to short intergrowths surrounding the particles, while spherical HfO 2 nanocrystals from the solvent-controlled synthesis led to the formation of long stacking faults in the YBCO matrix. It means that the small difference in crystallinity, lattice parameters, nanocrystal structures, core diameter of preformed nanocrystals in colloidal solutions have a strong influence on the formation of the structural defects around the particles in the YBCO matrix, leading to different pinning performances. 

We examine the capacity of the Large Hadron Collider to determine the mean proper lifetime of long-lived particles assuming different decay final states. We mostly concentrate on the high luminosity runs of the LHC, and therefore, develop our discussion in light of the high amount of pile-up and the various upgrades for the HL-LHC runs. We employ model-dependent and model-independent methods in order to reconstruct the proper lifetime of neutral long- lived particles decaying into displaced leptons, potentially accompanied by missing energy, as well as charged long- lived particles decaying ihnto leptons and missing energy. We also present a discussion for lifetime estimation of neu- tral long-lived particles decaying into displaced jets, along with the challenges in the high PU environment of HL-LHC. After a general discussion, we illustrate and discuss these methods using several new physics models. We conclude that the lifetime can indeed be reconstructed in many concrete cases. Finally, we discuss to which extent including timing information, which is an important addition in the Phase-II upgrade of CMS, can improve such an analysis.