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We analyze here how pre-service teachers explicate their views about the wave-particle duality of photons and what role it plays in their arguments supporting the quantum nature of light. The data for the analysis is provided by 12 written reports about the double-slit experiment with feeble light. The analysis is based on constructing semantic networks corresponding to pre-service teachers' written texts. Contingency-like associative correlation between word-pairs is used to differentiate between word-pairs, where associations of two terms or words is systematic. Such associations indicate connections, which are significant for key term vocabularies in construction of inferences and arguments. Based on that information of the key vocabulary we then construct the structure of pre-service teachers' argument for the nature of the photon and its wave-particle duality, in the form of directed argument graphs (DAGs). The results show that argument structures in four to six out of 12 cases meet the goals set for pre-service teacher education. In these cases, experimental aspects and wave-particle duality play an important role in the pre-service teachers' argument and its structure.

Dissipative particle dynamics (DPD) is a widespread computational tool to simulate the behavior of soft matter and liquids in and out of equilibrium. Although there are many applications in which the effect of temperature is relevant, most of the DPD studies have been carried out at a fixed system temperature. Therefore, this work investigates how to incorporate the effect of system temperature variation within the DPD model to capture realistic temperature-dependent system properties. In particular, this work focuses on the relationship between temperature and transport properties, and therefore, an extended DPD model for transport properties prediction is employed. Transport properties, unlike the equilibrium properties, are often overlooked despite their significant influence on the flow dynamics of non-isothermal mesoscopic systems. Moreover, before simulating the response of the system induced by a temperature change, it is important to first estimate transport properties at a certain temperature. Thus here, the same fluid is simulated across different temperature conditions using isothermal DPD with the aim to identify a temperature-dependent parametrization methodology, capable of ensuring the correctness of both equilibrium and dynamical properties. Liquid water is used as a model system for these analyses. This work proposes a temperature-dependent form of the extended DPD model where both conservative and non-conservative interaction parameters incorporate the variation of the temperature. The predictions provided by our simulations are in excellent agreement with experimental data. [ABSTRACT FROM AUTHOR]

The summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,717 new measurements from 869 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Most of the 120 reviews are updated, including many that are heavily revised. The is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete (both volumes) is published online on the website of the Particle Data Group () and in a journal. Volume 1 is available in print as the . A with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app. The 2024 edition of the Review of Particle Physics should be cited as: S. Navas et al. (Particle Data Group), Phys. Rev. D 110, 030001 (2024) © 20242024

Climate change will impact mountain watershed streamflow both directly—with changing precipitation amounts and variability—and indirectly—through temperature shifts altering snowpack, melt, and evapotranspiration. To understand how these complex processes will affect ecosystem functioning and water resources, we need tools to distinguish connections between water sources (rain/snowmelt), groundwater storage, and exit fluxes (streamflow/evapotranspiration), and to determine how these connections change seasonally and as climate shifts. Here, we develop novel watershed-scale approaches to understand water source, storage, and exit flux connections using a dynamic-flux particle tracking model (EcoSLIM) applied in California's Cosumnes Watershed, which connects the Sierra Nevada and Central Valley. This work develops new visualizations and applications to provide mechanistic understanding that underpins the interpretation of isotopic field data at watershed scales to distinguish sources, flow paths, residence times, and storage selection. In our simulations, streamflow comes primarily from snow-derived water while evapotranspiration generally comes from rain. Most streamflow starts above 1,000 m while evapotranspiration is sourced relatively evenly across the watershed and is generally younger than streamflow. Modeled streamflow consists primarily of water sourced from precipitation in the previous 5 years but before the current water year, while ET consists primarily of water from precipitation in the current water year. ET, and to a lesser extent streamflow, are both younger than water in groundwater storage. However, snowmelt-derived streamflow preferentially discharges older water from snow-derived storage. Dynamic-flux particle tracking and new approaches presented here enable novel model-tracer comparisons in large-scale watersheds to better understand watershed behavior in a changing climate.