Comparison of camera traps, eDNA, and visual encounter surveys for threatened species detection
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Here, it has been proposed that at high densities nuclear matter will consist of a Fermi sea of quarks surrounded by a small shell of confined baryon at the large momenta, so called Quarkyonic matter. In this contribution we will discuss an alternative configuration, dubbed Baryquark matter, which in a sense is a complement of Quarkyonic matter. Baryquark matter consists of a Fermi sea of confined baryons surrounded by a shell of deconfined quarks. Following Koch and Vovchenko (2023) we will show that for certain (simplified) implementations Baryquark matter is energetically favored over Quarkyonic matter. We will then briefly discuss how the inclusion of the quark structure of nucleons will lead to a configuration which resembles the picture of Quarkyonic matter.
Direct ink writing (DIW) of polymer nanocomposites with high loadings of two-dimensional (2D) nanofillers (graphene and hexagonal boron nitride (hBN)) is challenging because of potential clogging, use of hazardous solvents, and agglomeration. Here, in this work, a shear exfoliation and sieving method to prepare DIW ink with high loading of nanofillers produced from low-cost bulk layered materials such as graphite and bulk hBN powder for successful DIW printing without the use of any solvents, binders, or plasticizers. The single-step exfoliation technique resulted in a composite with substantial layer reduction along the c-axis, as confirmed by SEM, TEM, XRD, and Raman analysis. Incorporating exfoliated graphene (40 wt%) increased viscosity by ∼6 orders of magnitude due to enhanced particle–matrix interactions, leading to pronounced yield stress behavior and a yield stress of approximately 1598 Pa, which enabled excellent shape retention during extrusion. Using the DIW technique, porous structures such as desalination membranes, self-sensing bone scaffolds, thermal management coating, and serpentine strain sensors were fabricated. When tested in a direct contact membrane distillation setup, the fabricated membrane demonstrated a promising permeate flux of 21.85 Lm −2 h −1 and a salt rejection of 74.3 %. The fabricated serpentine sensor exhibited stable signal variations under cyclic tensile loading, with a working range of 0–200 % strain and a maximum gauge factor of 43,735. A cell culture test using the printed bone scaffold demonstrated promising cell attachment and proliferation. The DIW printed hBN nanocomposite exhibited reversible shape change under heat, demonstrating potential 4D printing capability and efficient thermal management when exposed to high heat or flame.
A next-generation medium-energy gamma-ray telescope targeting the MeV range would address open questions in astrophysics regarding how extreme conditions accelerate cosmic-ray particles, produce relativistic jet outflows, and more. One concept, AMEGO-X, relies upon the mission-enabling CMOS Monolithic Active Pixel Sensor silicon chip AstroPix. AstroPix is designed for space-based use, featuring low noise, low power consumption, and high scalability. Desired performance of the device include an energy resolution of 5 keV (or 10% FWHM) at 122 keV and a dynamic range per-pixel of 25–700 keV, enabled by the addition of a high-voltage bias to each pixel which supports a depletion depth of 500 μ m. This work reports on the status of the AstroPix development process with emphasis on the current version under test, version three (v3), and highlights of version two (v2). Version 3 achieves energy resolution of 10.4 ± 3.2% at 59.5 keV and 94 ± 6 μ m depletion in a low-resistivity test silicon substrate.
To meet the demands of the Beam Energy Scan phase-II (BES-II) program, the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) developed a dual real-time framework consisting of a High Level Trigger (HLT) and an Express Data Production system (xProduction). The HLT operates online within the Data Acquisition (DAQ) chain on a dedicated multi-core CPU cluster with the option to offload compute-intensive kernels to Xeon Phi coprocessors. It uses parallelized algorithms, such as the Cellular Automaton (CA) Track Finder, to perform rapid tracking, vertexing, and event filtering. This allows it to select events of interest in real time and provide immediate feedback on detector and beam conditions. In contrast, the xProduction workflow runs concurrently and independently of the DAQ loop. It applies near offline-quality calibration and reconstruction within hours of data collection. The xProduction input is the express data stream, whose content can be enriched by HLT trigger/priority selections under DAQ/HLT resource constraints, and it uses the STAR calibration/conditions framework, incorporating online calibration/QA information when available. This enables early preliminary physics analysis, including the reconstruction of rare signals, such as hyperons and hypernuclei. It also provides collaboration-wide access to analysis-ready datasets. Together, the HLT and xProduction systems form a complementary architecture: the HLT performs online event selection while the xProduction chain delivers high-quality results within a short amount of time. This integrated framework has enabled the prompt reconstruction of the $^5_Λ$ He hypernucleus with high statistical significance and the efficient processing of hundreds of millions of heavy-ion collision events. In conclusion, its demonstrated scalability and robustness establish a model for future high-luminosity experiments requiring both online event filtering and rapid access to analysis-quality data.
We report that the International Atomic Energy Agency (IAEA) inspectors routinely carry out environmental sampling (ES) as a verification method. Collection of environmental swipe samples at various locations in Gas Centrifuge Enrichment Facilities (GCEPs) is an important process in detecting misuse of a declared facility and possibly the existence of undeclared nuclear material. These samples are measured for isotopic composition in uranium containing particles by Thermal Ionization Mass Spectrometry (TIMS) or Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Even though ES is highly effective in detecting the absolute value of enrichments and their deviations from the declared values, it cannot explain the cause of those changes. Several potential explanations can serve as possibilities for particles detected above or other than the declared enrichment. These include normal and non-malicious events such as the design of the enrichment cascades, unintentional failures of the machines, or overshoot during startup of a cascade. It can also be the result of deliberate misuse by the facility operators. The primary objective of this work is to understand how these factors affect the enrichments produced by a cascade and quantify anticipated multi-isotopic concentrations for each case. The following methodology is employed to determine signatures at a particular facility. 1) Utilize a new two-dimensional multi-component diffusion code to obtain centrifuge performance data and use that information to design and perform cascade analysis. Compare and contrast the results with previous 1-D radially averaged solutions from the Pancake code. 2) Design a GCEP cascade with the production goal of 19.75% 235 U for each set of machine data above. Investigate two cascade scenarios that include enrichment of natural uranium (NU) feed to 19.75% 235U in a single cascade compared to a two-step process of NU to 5% and then 5% to 19.75%. 3) Simulate the intentional vs. unintentional off-normal scenarios in the cascades to assess the differences in isotopic concentrations. A non-ideal squared-off cascade model developed at the University of Virginia is used to calculate flow rates and isotopic concentrations of the process gas. The analysis is performed using the Rome machine model operated at 600 m/s rotor speed. The upper and lower bounds of normal and abnormal enrichments in a typical facility are used in conjunction with ES results to understand the root causes of such observations.
Meson-baryon systems with strangeness content provide a unique laboratory for investigating the strong interaction and testing theoretical models of hadron structure and dynamics. In this work, the measured correlation functions for oppositely charged $Ξ$-K and $Ξ$ − 𝜋 pairs obtained in high-multiplicity pp collisions at $\sqrt{𝑠}$ = 13 TeV at the LHC are presented. For the first time, high-precision data on the $Ξ$-K interaction are delivered at small relative momenta. The scattering lengths, extracted via the Lednický–Lyuboshits expression of the pair wavefunction, indicate a repulsive and a shallow attractive strong interaction for the $Ξ$-K and $Ξ$ − 𝜋 systems, respectively. The $Ξ$(1620) and $Ξ$(1690) states are observed in the $Ξ$ − 𝜋 correlation function and their properties, mass and width, are determined. These measurements are in agreement with other available results. Such high-precision data can help refine the understanding of these resonant states, provide stronger constraints for chirally motivated potentials, and address the key challenge of describing the coupled-channel dynamics that may give rise to molecular configurations .
In this letter, the first measurement of the femtoscopic correlation of protons and $Σ^+$ hyperons is presented and used to study the p–$Σ^+$ interaction. The measurement is performed with the ALICE detector in high-multiplicity triggered pp collisions at $\sqrt{s}$ = 13 TeV. The $Σ^+$ hyperons are reconstructed using a missing-mass approach in the decay channel to p + π 0 with π 0 → $γγ$, while both $Σ^+$ and protons are identified using a machine learning approach. These techniques result in a high reconstruction efficiency and purity, which allows the measurement of the p–$Σ^+$ correlation function for the first time. Thanks to the high significance achieved in the p–$Σ^+$ correlation signal, it is possible to discriminate between the predictions of different models of the N–$Σ$ interaction and to accomplish a first determination of the p–$Σ^+$ scattering parameters.
The first results of K*(892) ± production at midrapidity (|y| < 0.5) in pp collisions at $\sqrt{s}$= 13 TeV as a function of the event multiplicity are presented. The K*(892) ± has been reconstructed via its hadronic decay channel K*(892) ± → π ± + K$^0_{\textrm{S}}$ using the ALICE detector at the LHC. For each multiplicity class, the differential transverse momentum (p T ) spectrum, the mean transverse momentum ⟨p T ⟩, the p T -integrated yield (dN/dy), and the ratio of the K*(892) ± to K$^0_{\textrm{S}}$ yields are reported. These are consistent with previous K*(892) 0 resonance results with a higher level of precision. Comparisons with phenomenological models such as PYTHIA6, PYTHIA8, EPOS-LHC, and DIPSY are also discussed. For the first time, a significant K*(892) ± /K$^0_{\textrm{S}}$ suppression in pp collisions is observed at a 7σ level passing from low to high multiplicity events. The ratios of the p T -differential yields of K*(892) ± and K$^0_{\textrm{S}}$ in high and low multiplicity events are also presented along with their double ratio. For p T ≲ 2 GeV/c this double ratio persists below unity by more than 3σ suggesting that the suppression affects mainly low p T resonances. The measured decreasing trend of the K*(892) ± /K$^0_{\textrm{S}}$ ratio with increasing multiplicity, which in heavy-ion collisions is typically attributed to the rescattering of decay particles of the short-lived resonances, is reproduced by the EPOS-LHC model without the use of hadronic afterburners.
Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon de- grees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental QCD-defined densities that characterize the micro- scopic structure of hadronic systems. Recent advances in AI and machine learning have opened new avenues for addressing this challenge using deep learning techniques. A particularly promising direction is the integration of complex theoretical calculations and experimental simulations into a unified framework capable of reconstructing these densities directly from event-level information. In this document, we introduce a key algorithm called LOITS, which enables differentiable program- ming within such a framework, facilitating the use of AI/ML techniques to solve the inverse problem of QCF reconstruction at the event level.
Herein it is well known that catalytic centers containing the zinc(II) ion can act as both Lewis and Bronsted-Lowry acids. In addition to coordination number, the Zn coordination geometry can also strongly impact the acidity of the active site, no matter what measure of acidity is considered. Herein, we report the first pentacoordinate, zinc-ammonia complex containing a pyridine based tetraazamacrocycle, [(pyclen)Zn(NH 3 )](PF 6 ) 2 , that has applications in Lewis acid catalysis. From this structure, we obtain binding energies and acidities for a series of related pyclen and cross-bridged cyclen type tetraazamacrocycles comprising the pentacoordinate N 4 Zn(II)–OH 2 entity, collectively referred to as [(R-cyclen)Zn–OH 2 ] 2+ . Results from gas- and aqueous-phase density functional theory (M05-2X) and ab initio (MP2) calculations reported herein demonstrate that molecular geometry has a substantial influence on both the Zn–OH x binding strengths and the deprotonation energy of coordinated H 2 O, but not necessarily in a predicable way. While generally more constrained N–Zn–N coordination leads to greater Zn–OH 2 binding energies (Lewis acidities), the corresponding Lewis acidities of the complexes don’t always correlate with the Zn–OH 2 bond lengths nor the (Bronsted) acidity of the coordinated H 2 O. Additionally, the order of the Lewis acid strength of the [(R-cyclen)Zn(II)] complexes changes as the basic –OH 2 ligand is replaced with its –OH counterpart.
High explosives (HEs) are used in a diverse range of applications in which they could be exposed to various radiation levels that may cause potential chemical changes. This study further evaluated pentaerythritol tetranitrate (PETN) that was previously aged with a low-level 2 kGy dose of gamma irradiation in order to understand chemical changes caused by irradiation. Both unirradiated PETN and gamma-irradiated PETN were analyzed using ultra high-pressure liquid chromatography coupled to quadrupole time of flight mass spectrometry (UHPLC-QTOF). The resulting data were processed in a non-targeted manner using Fisher's ratio analysis and multivariate curve resolution-alternating least squares (MCR-ALS) to aid in discovery and identification of the chemical changes brought about by irradiation without a priori knowledge. The application of using UHPLC-QTOF in combination with chemometric techniques for the analysis of irradiated samples has not previously been performed. Here, in this work, we show how to use this method to provide chemical information that would otherwise not be discernible, such as the discovery of degradation of the various homologues of PETN. Major differences identified with radiolytic aging of the PETN sample included decomposition products that resulted from the degradation of the trigger linkage – the O–NO 2 bonds – resulting in the formation of alcohol and aldehyde groups. Similar degradation was also observed in the PETN homologues as well as interconversion from one homologue to another.
The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization, leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems. This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in energy, (ii) an in-depth evaluation of the evolution and viability of blockchain initiatives in energy with the help of expert surveys, and (iii) a novel decision-making model using a q-rung orthopair fuzzy Multi-Attributive Border Approximation (q-ROF-MABAC) method under the Einstein operator. The results were compared with existing decision models to validate consistency and robustness. Nine key blockchain use case categories were identified and ranked based on technical, economic, and governance dimensions. The results demonstrated that integrating expert insights into a fuzzy logic framework helps filter out overhyped claims in the literature and prioritize realistic and high-impact applications such as green certificates, grid services, and peer-to-peer energy trading. The model’s rankings remained stable across varying weight configurations, confirming the robustness of the methodology. This study provides an evidence-based decision-support tool for researchers, industry stakeholders, and policymakers to better understand, evaluate, and adopt blockchain technologies in the energy sector.
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many promising features in studying materials including high spatial resolution and high mass accuracy of elements, molecules, and isotopes. Its ability to resolve isotopes is especially attractive in studying transmutation products of single crystal tungsten (SCW) post neutron irradiation. Tungsten (W) is a contender of plasma facing materials (PFMs) due to its high thermal and radiological stability. PFMs to be used in the construction of fusion vessels are subject to high temperature and neutron irradiation, resulting in changes to materials including transmutation, which ultimately impact material mechanical and thermal properties. We used IONTOF TOF.SIMS V instrument equipped with a 30 keV Bi 3 + primary ion beam to study pristine SCW and irradiated SCW speciemens. Scanning electron microscope coupled with focused ion beam (SEM-FIB) was used to reduce the dosage of neutron irradiated tungsten and prepare for specimens for SIMS analysis. Static ToF-SIMS spectra were obtained, and transmutation product peak identification was presented in this work. Identified molecules and molecular fragments were compared against isotope theoretical mass to charge ratios of tungsten, rhenium, osmium, and other relevant products. Our results show that ToF-SIMS provides a viable means to study transmutation products of W post neutron irradiation. Such applications are suitable to investigate transmutation effects on materials that are being considered and developed for fusion pilot plants.
This study presents a multi-stage, cross-validation comparison of three software packages for Monte Carlo ray tracing (MCRT) applied to central tower concentrating solar power (CSP) systems. The three packages evaluated are: (1) SolTrace, an open-source tool developed by the National Renewable Energy Laboratory (NREL); (2) Solstice, an open-source program created by CNRS-PROMES and Meso-Star, with enhancements for CSP applications (called solsticepy) from the Australian National University; and (3) TieSOL, a commercial software developed by Tietronix. This investigation extends previous ray tracing comparisons by incorporating models of multi-facet heliostats within a commercial-scale solar field, taking into account zoned focal lengths and canting configurations. Receiver flux distributions were compared across the tools using a series of case studies, including single-heliostat scenarios, isolated blocking situations, and comprehensive full-field simulations. The case studies were designed to diagnose differences across the models at varying levels of complexity, and to identify and resolve discrepancies as additional parameters were introduced. Key factors examined in the analysis include sun positions, heliostat location, facet and canting focus, and aimpoint strategies. The comparison aims to improve the accuracy and reliability of these tools while providing benchmark cases for validating future optical modeling tools.
Thermal runaway (TR), characterized by rapid exothermic reactions, presents a serious safety risk in lithium-ion batteries (LiBs). External triggers such as high temperatures, mechanical abuse, or internal short circuits (ISCs) can initiate TR, often resulting in sidewall rupture, which may escalate to catastrophic battery pack failure. In this study, we developed and applied high-speed synchrotron imaging techniques to investigate sidewall rupture mechanisms in LiBs subjected to different triggering scenarios. Using in situ 4D tomographic imaging, we visualized the dynamic evolution of sidewall rupture with high spatial and temporal resolution. The results revealed distinct failure behaviors linked to each trigger, underscoring the complex and condition-specific nature of sidewall breach and battery failure. These insights highlight the critical importance of implementing tailored safety strategies across diverse applications. Our findings demonstrate the powerful potential of synchrotron high-speed tomography as a diagnostic tool for advanced safety testing and cell qualification.
Metabolite fluctuations following nutrient metabolism or environmental stresses impact various intracellular signaling networks and stress responses to maintain cellular and organismal homeostasis. It has been shown that subcellular organelles, such as the endoplasmic reticulum, the Golgi apparatus, lysosomes and mitochondria serve as crucial hubs linking alterations in metabolite levels to cellular responses. This role is coordinated by molecular machineries that are associated with the lipid membranes of organelles, which sense the fluctuations in specific metabolites and activate the appropriate signaling and effector molecules. Moreover, recent studies have demonstrated that membraneless organelles, such as the nucleolus and stress granules, are involved in the metabolic stress response. Metabolite-induced post-translational modifications appear to play an important role in this process. Here, we review the molecular mechanisms of metabolite sensing and metabolite-mediated stress responses through membrane-bound and membraneless organelles in mammalian cells.
This paper reviews current theoretical and numerical approaches to optimization problems governed by partial differential equations (PDEs) that depend on random variables or random fields. Such problems arise in many engineering, science, economics and societal decision-making tasks. This paper focuses on problems in which the governing PDEs are parametrized by the random variables/fields, and the decisions are made at the beginning and are not revised once uncertainty is revealed. Examples of such problems are presented to motivate the topic of this paper, and to illustrate the impact of different ways to model uncertainty in the formulations of the optimization problem and their impact on the solution. A linear–quadratic elliptic optimal control problem is used to provide a detailed discussion of the set-up for the risk-neutral optimization problem formulation, study the existence and characterization of its solution, and survey numerical methods for computing it. Different ways to model uncertainty in the PDE-constrained optimization problem are surveyed in an abstract setting, including risk measures, distributionally robust optimization formulations, probabilistic functions and chance constraints, and stochastic orders. Furthermore, approximation-based optimization approaches and stochastic methods for the solution of the large-scale PDE-constrained optimization problems under uncertainty are described. Some possible future research directions are outlined.