The University of Kansas: Journals@KU
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Quantum mechanical aspects of coherent photoproduction: the limits of coherence, and multiple vector mesons
Get PDFQuantum mechanics is central to coherent photoproduction in ultra-peripheral collisions (UPCs). This writeup will discuss some surprising aspects of UPCs that stem from these quantum mechanical roots. The Good-Walker (GW) paradigm, which connects coherent photoproduction with the target nucleus remaining in its ground state. This contrasts with a semi-classical picture, where coherence depends on the positions of the individual nucleons and the momentum transfer. Unlike the GW approach, the semiclassical picture is consistent with the observed data on coherent photoproduction with nuclear breakup, and with coherent photoproduction in peripheral collisions. The semiclassical approach allows for a wider variety of coherent UPC reactions, such as coherent photoproduction of charged mesons, including some non-quark-antiquark exotica. Quantum mechanics is also key to the coherent photoproduction of multiple vector mesons by the interactions of a single ion pair. The vector mesons share a common impact parameter, and so can exhibit richer interference patterns than single mesons. At forward rapidities, the cross sections to produce multiple identical vector mesons are enhanced due to superradiance. With enough statistics, multi-meson events may provide an opportunity to observe stimulated decays
Four-pion state in UPC
Get PDFThe production of four pion events in ultraperipheral heavy-ion collisions at RHIC and LHC energies is studied. Preliminary H1 data is used to enhance the understanding of the poorly known process. Predictions for photon-nucleus interactions are calculated for various excited states of mesons. Agreement between theoretical predictions and available STAR data at RHIC is presented. The comparison of the four-pion invariant mass spectrum and nuclear total cross section indicates that rho(1570) plays a crucial role in accurately describing existing experimental data. Nuclear predictions for LHC energy in the central region of rapidity are also provided.
Exclusive η_c production by γ*γ interactions in electron-ion collisions
Get PDFOne of the main goals of future electron-ion colliders is to improve our understanding of the structure of hadrons. We study the exclusive ηc production by γγ* interactions in eA collisions and demonstrate that future experimental analysis of this process can be used to improve the description of the transition form factor. The rapidity, transverse momentum, and photon virtuality distributions are estimated considering the energy and target configurations expected to be present at the EIC, EicC, and LHeC and assuming different predictions for the light-front wave function of the meson. Our results indicate that electron-ion colliders can be considered an alternative for providing supplementary data to those obtained in colliders
Book Review of Population and Labor Market Policies in China\u27s Reform Process by Wenkai Sun
No full textWelcome to the machine: A Pan-Continental overview machine learning applications in ecology and conservation
Get PDFMachine-learning emerged as an excellent alternative to understanding ecological patterns and processes at different spatiotemporal scales. Our study aimed to offer a pictorial overview of the status quo on the use of machine-learning in ecology and conservation globally. Using keywords in the Scopus engine, we indexed all publications in ecology and conservation using machine-learning. We employed descriptive statistics and regressions models to provide an overview and predict geopolitical patterns. The majority of manuscripts were condensed in economically affluent countries, such as the United States (USA) and China (CHN) which together amount to 91 (36.8%) studies. There is a spatial aggregation in the authors’ affiliations, once 182 (73.7%) studies derived from both Nearctic and Palearctic teams, whereas Tropical teams published 65 (26.3%) manuscripts and the most-cited papers also are concentrated in northern regions. In ecology and conservation, machine-learning first appear in the literature in 2003. Yet, increased exponentially since the 2010s. In 2010, this overview indicated nine manuscripts, whereas 10-yrs later reached 120 publications. Most studies (N = 173; 70.1%) are focused on landscape and vertebrate ecology. The primary aims of the publications were widely variable but strongly adherent to providing the best-information on both landscape-scale classifications and species distribution modeling. The manuscripts encompass different methods, from maximum entropy to boosted regression trees and random forest, sometimes using a gamma of deep-learning architectures. Finally, the predictive variables (i.e., mammal diversity and per capita GDP) do not exert significant influences on the number of studies published
Functional diversity rather than species diversity can be accurately assessed by remote sensing in sandy grassland
No full textThe prediction of grasslands plant diversity using satellite images has been intensively studied. However, the accuracy of functional diversity (FD) is still unknown. Therefore, high spatial resolution Worldview-3 (WV-3) multiple spectral data were used to predict species and FD at the pixel scale (1.2 × 1.2 m) over central Hunshandak Sandland, Inner Mongolia, north China. Data acquired from 120 field plots (6 × 6 m) were used to train and validate several statistical learning methods with a primary objective of linking the satellite spectral and texture indices to the plant diversity indices. Among the several diversity indices tested, functional trait diversity, in particularly Functional Attribute Diversity (FAD1), Modified Functional Attribute Diversity (MFAD) were best predicted (coefficient of determination approximately 0.29 and 0.14, respectively, n=48) using texture indices. However, species diversity (richness, H, E, or D) and other FDs haven’t not been well predicted by WV-3 data. WV data did not significantly improve the prediction accuracy for plant diversity in sandy grassland. Further, high plot-level vegetation coverage can improve the performance of spectral indices for predicting H, E, D and FD. These results highlighted the assessing variability across field conditions and demonstrated the capacity of high spatial-spectral satellite images to monitor plant functional diversity in sandy grasslands
Improving the standardization of wild bee occurrence data: Towards a formal wild bee data standard
Get PDFConservation and management of wild bees is hindered by the variety of ways wild bee occurrence data are recorded, managed, and shared. Here, we present solutions to address this issue and introduce The Wild Bee Data Standard, a standardized means of recording and reporting data associated with wild bee occurrences, including physical specimens and photo observations. This standard aligns with contemporary data management practices widely adopted by the broader biodiversity data community. We propose a set of terms for the standard that describe various features of bee occurrences, including collection method and location, taxonomic verification, and final record storage. We emphasize the importance of providing sampling protocol and effort information with wild bee occurrence data and offer guidance to make this a more common practice. We describe how to translate data not currently aligned with the standard to meet its conditions, and how to upload those data to an accessible online repository. We provide case studies, data entry templates, a glossary of terms, and additional resources to guide new users to implementing the standard. We also present a forum, established as a GitHub repository, to support continued development of the standard. Recognizing the significant change this represents for current data practices, we outline the benefits for the bee research and conservation community that will result from improved data standards. We advocate for making all historical, current, and future bee occurrence data openly available to facilitate more rigorous and comprehensive research, conservation, and management of wild bees. This contribution is part of a series developed in association with the U.S. National Native Bee Monitoring Network to standardize bee monitoring practices
Creating Fair Game, an Online Computer Game on Fair Use
Get PDFFair Game is an online computer game that teaches undergraduate students about ethically using copyrighted materials in course assignments. The game, created in a collaboration between Augusta University Libraries and the Center for Instructional Innovation, focuses on the doctrine of fair use and its four factors. This article presents a case study on the game’s design and development following a recommended practice from the literature: mapping an information literacy model to gameplay using the game design theory of aligning game mechanics to learning mechanics. The authors discuss their experience with mapping portions of the Association of College and Research Libraries framework (2015) and using the Learning Mechanics–Game Mechanics Model (Arnab et al., 2015) for designing Fair Game. The authors recommend future game makers also consider aligning motivational learning theories to game design
