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The proliferation of plastic pollution has led to the widespread accumulation of microplastics (MPs) in marine ecosystems. While surface sediment contamination is relatively well studied, knowledge of vertical MP distri- bution within sediment columns remains limited. This study examines the abundance, vertical distribution, and characteristics of MPs in subtidal and intertidal sediments of Panjang Island, Java Sea. Fifteen shallow (10 cm) and three deep (~100 cm) sediment cores were analyzed for MP abundance, morphology, size, color, and polymer using microscopy and ATR-FTIR. MPs were detected in all cores, with an average concentration of 0.49 ± 0.28 MPs g⁻¹ in surface sediments. The highest surface concentration (2.08 ± 0.22 MPs g⁻¹) occurred in the southwest, a sheltered site with greater anthropogenic influence, while the lowest (0.05 ± 0.07 MPs g⁻¹) was recorded in the northwest, a remote and less disturbed area. Fibers dominated particle types. White, black, and blue were the most common colors, and size distributions were skewed toward particles <1 mm. Polypropylene and polyethylene were the most frequent polymers, reflecting their widespread use and persistence. Vertical profiles revealed higher MP concentrations near the surface, indicating intensified inputs in recent decades. No MPs were detected below 70 cm, suggesting limited downward migration and marking the onset of contami- nation during the plastic era. This study also found MPs in deeper sediment layer, likely due to post-depositional processes such as bioturbation. These findings demonstrate that sediment cores serve as valuable archives of historical MP deposition, capturing both global production trends and local environmental influences, and provide a basis for targeted management strategies. 

We construct uniformly bounded solutions of the equation div u = f for arbitrary data f in the critical spaces Ld(Ω), where Ω is a domain of Rd. This question was addressed by Bourgain & Brezis, [BB2003], who proved that although the problem has a uniformly bounded solution, it is critical in the sense that there exists no linear solution operator for general Ld-data. We first discuss the validity of this existence result under weaker conditions than f ∈ Ld(Ω), and then focus our work on constructive processes for such uniformly bounded solutions. In the d = 2 case, we present a direct one-step explicit construction, which generalizes for d > 2 to a (d − 1)-step construction based on induction. An explicit construction is proposed for compactly supported data in L2,∞(Ω) in the d = 2 case. We also present constructive approaches based on optimization of a certain loss functional adapted to the problem. This approach provides a two-step construction in the d = 2 case. This optimization is used as the building block of a hierarchical multistep process introduced in [Tad2014] that converges to a solution in more general situations. 

The exploration of complex physical or technological processes usually requires exploiting available information from different sources: (i) physical laws often represented as a family of parameter dependent partial differential equations and (ii) data provided by measurement devices or sensors. The amount of sensors is typically limited and data acquisition may be expensive and in some cases even harmful. This article reviews some recent developments for this “small-data” scenario where inversion is strongly aggravated by the typically large parametric dimension- ality. The proposed concepts may be viewed as exploring alternatives to Bayesian inversion in favor of more deterministic accuracy quantification related to the required computational complexity. We discuss optimality criteria which delineate intrinsic information limits, and highlight the role of reduced models for developing efficient computational strategies. In particular, the need to adapt the reduced models—not to a specific (possibly noisy) data set but rather to the sensor system—is a central theme. This, in turn, is facilitated by exploiting geometric perspectives based on proper stable variational formulations of the continuous model. 

Abstract Lake Magadi is an internally drained, saline and alkaline terminal sump in the southern Kenya Rift. Geochemistry of samples from an ~200 m core representing the past ~1 m.y. of the lake's history shows some of the highest concentrations of transition metals and metalloids ever reported from lacustrine sediment, including redox-sensitive elements molybdenum, arsenic, and vanadium. Elevated concentrations of these elements represent times when the lake's hypolimnion was euxinic—that is, anoxic, saline, and sulfide-rich. Euxinia was common after ca. 700 ka, and after that tended to occur during intervals of high orbital eccentricity. These were likely times when high-frequency hydrologic changes favored repeated episodes of euxinia and sulfide precipitation. High-amplitude environmental fluctuations at peak eccentricity likely impacted water balance in terrestrial habitats and resource availability for early hominins. These are associated with important events in human evolution, including the first appearance of Middle Stone Age technology between ca. 500 and 320 ka in the southern Kenya Rift. 

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours. 

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on ${}^{40}\mathrm{Ar}$and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called “brems flipping,” as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE’s burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage. Published by the American Physical Society2025 

Abstract This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of thedE/dxmodel on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.