bluuebunny/crossref_metadata_2025 huggingface dataset:
On the association between students’ (domain-specific) subjective well-being and academic achievement—disentangling mixed findings
Authors: Maechel Lena , Steinmayr Ricarda , Christiansen Hanna , Wirthwein Linda
Published: December 2022

Today, there is wide recognition of schools as places that should foster children’s learning and well-being alike. The past two decades have thus witnessed an upsurge in research on children’s subjective well-being (SWB) in school and how it relates to their academic achievement. The first meta-analysis on the SWB/achievement relation revealed substantial, yet unexplained variability in effect sizes across samples and studies. In this context, in the present investigation we examined three potential sources of differential SWB/achievement relations: The domain-specificity of measures (global/school-specific/math-specific), the type of SWB component (cognitive/affective), and the type of achievement indicator (test-based/grade-based). Data from N = 767 8th and 9th grade students from German secondary schools were assessed via latent factor analyses. Our findings generally point towards stronger SWB/achievement relations with a more specific and specificity-matching rather than a less specific and specificity-mismatched SWB measure, with SWB’s cognitive rather than its affective component, and with a grade- rather than test-based indicator of academic achievement. We discuss differences in our findings by type of SWB component as well as by level of domain-specificity, and we provide initial empirical evidence supporting the relevance of SWB’s subject-specific measures.
Biomarkers and translational research approaches in breast cancer—an update
Authors: Starzer Angelika M. , Berghoff Anna S. , Bartsch Rupert
Published: December 2022

Diagnosis and decision-making in the treatment of breast cancer patients is vastly dependent on the exploration of biomarkers. Estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 are long-standing biomarkers, which determine the breast cancer subtype. In current practice, gene expression analyses further define the molecular breast cancer subtype and give additional information on disease characteristics. Prognostic biomarkers provide information regarding recurrence risk and survival. Predictive biomarkers, such as programmed cell death ligand 1 expression, are tools for identifying patients who can benefit from specific therapy regimens in order to choose the best treatment option for the patient. While some biomarkers are affordable and readily available, others remain technically complex to access. Translational research builds the bridge from discovering novel biomarkers in preclinical studies to testing their application utility in the clinical setting. Integrating translational studies into clinical trials is therefore essential to find novel and reliable biomarkers for an optimal personalized treatment approach for patients with breast cancer.
Linking gender, climate change and security in the Pacific Islands Region: A systematic review
Authors: Howard Elise
Published: December 2022

This systematic review aims to address gaps in understanding how concepts of gender, climate change and security are given meaning and linked in empirical scholarship within the Pacific Islands Region. The review assesses the 53 articles returned through Web of Science, SCOPUS and ProQuest databases that are derived from empirical research and refer to gender, climate change and security. The findings indicate that this is an emerging topic in a region that is one of the most vulnerable to climate change across the globe. Most frequently gender analysis is given superficial treatment; there is limited literature that connects gendered vulnerabilities to historical legacies and structural inequalities; and women’s critical roles that create security are often overlooked and devalued. The review indicates that greater work is needed to question perceived threats to security and to reveal how climate change, gendered institutions, systems and spaces, historical legacies and politics interact to construct security in the Pacific Islands Region.
Strategies to Build Trust and COVID-19 Vaccine Confidence and Engagement among Minority Groups in Scotland
Authors: Adekola Josephine , Fischbacher-Smith Denis , Okey-Adibe Thelma , Audu Jamila
Published: December 2022

As countries continue to deal with the global COVID-19 pandemic and its consequences, policymakers recognize that science, technology, and innovation (STI) practices offer a means of addressing many of the health problems that arise from the ongoing pandemic. Such recognition has given rise to many STI policy initiatives across various areas of science and policy, leading to a better understanding of coronavirus and the development of COVID-19 vaccines, treatments, and diagnostics. However, the practical implementation of vaccine and treatment strategies within local communities extends well beyond the laboratory. This study explored how misinformation and trust amplify or attenuate coronavirus and COVID-19 vaccine perceptions of those from ethnic minority groups deemed more susceptible to the impacts of the virus. Primary data in this study were collected in Scotland through semistructured interviews with 26 expert and nonexpert members from Scotland’s minority ethnic communities. The study findings show that risk perception is fluid and dependent on the information and evidential environment in which people find themselves. Misinformation, fake news, conspiracies, and trust or distrust (from prior experiences and historic practices) influence the perception of coronavirus and how risk messages are received, including the acceptance of coronavirus vaccines. This article reflects on Scotland’s approach to building trust and COVID-19 vaccine confidence and engagement based on the findings of this study, identifying areas of strength and areas for further improvement or research. The authors believe, as shown by our research, that vaccine engagement will be more impactful if developed by and with the public, and reflects public values, concerns, and priorities.
Degradation of amaranth by persulfate activated with zero-valent iron: influencing factors, response surface modeling
Authors: Yu Changye , Lu Xian , Lu Jinyu , Zhang Yinjiang
Published: December 2022

In this study, zero-valent iron (ZVI) is applied to activate persulfate (PDS) for the degradation of amaranth (AMR). The effects of PDS concentration, ZVI concentration, solution pH  , temperature, and reaction time on the degradation of AMR by the ZVI/PDS advance oxidation process are investigated. Sulfate and hydroxyl radicals are involved in the main reaction pathway of AMR and sulfate radical acts as a dominant oxidant. The CCD (central composite design) plan is chosen to build the RSM model for the prediction of AMR degradation. ANOVA analysis shows that the secondary fitting model had great fitness with R2 = 0.997, $$R_{{{\text{adj}}}}^{2}$$

R

adj

2

 = 0.936, p-value of lack of fit = 0.107. Optimum conditions for 98% AMR removal given by RSM are PDS concentration = 7.33 mM, ZVI dosage = 17.79 mM, initial pH   4.62, temperature = 59.49 °C, reaction time = 9.88 min which is proved to be very closed to the real removal rate of 96.78%. Sensitivity analysis indicates that the relative importance of the influencing parameters is of the following order: temperature, PDS concentration, pH  , ZVI dosage, and reaction time. The PDS/ZVI system shows an acceptable RSE of about 75% and TOC removal of 85% on AMR oxidation. Finally, the possible pathway of AMR degradation is proposed.
From 2D projections to the 3D rotation matrix: an attempt for finding a machine learning approach for the efficient evaluation of mechanical joining elements in X-ray computed tomography volume data
Authors: Schromm T. M. , Grosse C. U.
Published: December 2022

Destructive and predominantly manual procedures are commonly used in the automotive industry for the testing of mechanical joints, such as rivets or screws. Combining X-ray computed tomography (CT) and machine learning (ML) bears the potential of a non-destructive and largely automated methodology. Assuming the desired result is a comprehensible and documentable evaluation, three basic steps need to be automatized: First, a joint must be detected and identified as such in a CT scan of the joined parts. Second, the detected region containing the joint is rotated to a predefined orientation. Third, key measures in cross-sections from the newly oriented joint are dimensioned and documented. This work deals only with the second step, the rotation. On the one hand, we present a methodology for creating a well-curated data set for the contextual machine learning application. On the other, we evaluate its performance on the well-known ResNet50. More concretely, we investigate if it is possible for a deep convolutional neural network (CNN) to learn the respective rotation matrix from three volume projections that are perpendicular to each other. Two scenarios are investigated: In one scenario we assume that future data that is presented to the network has similar rivet demographics to historic data. We therefore do not employ hold-out sets for the network evaluation. In the other scenario we assume the opposite and therefore evaluating the networks performance with hold-out sets. We show that from a machine learning point of view, a CNN like ResNet50 is well able to learn this relationship with acceptable accuracy. In most cases the validation loss dropped below 0.1 after only a couple of epochs. In one particular case, we even reached both mean and median errors lower than 0.2 for approximately 80% of the entire test set of 1600 examples using our methodology. From an application point of view, however, these low test set errors should be treated with caution since small deviations from the intended rotation matrix can cause volume warping and translation. In another case, in which we used a hold-out set, only a fraction of the median errors were below 0.2.
Application of an Electric Impulse Technology tool for removing concrete or mineral surfaces and cutting concrete within existing structures
Authors: Voigt Matthias , Anders Erik , Hammel Lukas , Otto Jens , Will Frank
Published: December 2022

With the help of the Electric Impulse Technology, it is possible to remove concrete or mineral surfaces in existing building structures with low vibrations and emissions and to make openings by slotting and cutting. Very fast high-voltage impulses are generated and led to an electrode arrangement positioned on the material to be destroyed. A breakdown is generated in the material and thus the material is destroyed. On this basis, investigations into the removal of concrete as well as mineral structures are carried out and a machine concept for the use of the Electric Impulse Technology for operations in existing buildings is developed.
Modeling of CNC machine tools’ spatial geometric error based on two-dimensional angle error
Authors: Zhang Xiuqin , Chen Guohua , Zhang Lin , Hu Yongwen , Mao Jie , Zhang Zhiyang
Published: December 2022

A modeling method of CNC machine tools’ spatial geometric error is proposed based on two-dimensional angle error in this paper. Different from traditional modeling method, this method takes into account the effect of additional angle errors because the machine tool’s angle errors in the system of the lower guide rail will be passed on to slip board structure to the upper guide rail system. Firstly, after analyzing the principle of two-dimensional angle error, an optimized error compensation model of CNC machine tools’ spatial geometric error contains two-dimensional angle error is established through the traditional homogeneous coordinate transformation matrix method (HTM). Further, by applying the experiment, the compensation effect of the model is verified through comparing the body diagonal errors of CNC machine tools before and after compensation. Experiment results show that the body-diagonal accuracy after compensation based on the optimized model is 87.73% higher than that before compensation, and 54.82% higher than that of the traditional model after compensation.

Article Highlights

The highlight of the study reported here is threefold: Firstly, the principle of two-dimensional angle error is analyzed. Secondly, an optimized spatial geometric error compensation model of CNC machine tool is established based on the principle of two-dimensional angle error. Thirdly, the compensation effect of the optimized compensation model is verified by comparing the bodies diagonal precision of the machine tool.
Forging of an age-hardenable Mg–Al–Ca–Mn–Zn alloy on industrial scale
Authors: Papenberg Nikolaus , Hatzenbichler Thomas , Grabner Florian , Uggowitzer Peter J. , Pogatscher Stefan
Published: December 2022

Weight reduction plays an important role in transportation industries, directly impacting on fuel consumption and vehicle range. The use of multi-material mixes is common practice, allowing for an optimum application of specific material properties. Light metals, primarily aluminum alloys, are used in both, cast and wrought state, to good effect. On the other hand, magnesium alloys, which are still lighter by one third, are used in castings exclusively. While scientific research and development on Mg wrought alloys is progressing steadily, industrial implementation is still scarce. As a result, safety relevant and structural applications made from Mg wrought products are nearly nonexistent. To increase acceptance and facilitate industrial application for this interesting class of materials we investigated the forging process of an original-sized automotive control arm. To ease industrial access, the used age hardenable Mg–Al–Ca–Zn–Mn lean alloy, can be processed similarly to Al alloys, e.g. 6xxx series. This work describes the development sequence, starting with the analysis of the forming window, followed by laboratory forging trials and industrial sized part production, providing information on forming characteristics as well as possible difficulties.
Performance of a seismically isolated building in Albania during the Durrës sequence of 2019 and a feasibility study of pendulum isolators in the region
Authors: Hima Nikolin , Çaushi Ergys , Murtaj Luan , Castellano Maria Gabriella
Published: December 2022

This work evaluates the behaviour of the only seismically isolated building in Albania during the Durrës sequence of 2019 and analyses the efficiency and feasibility of pendulum isolators in the region. The behaviour of the existing building is analysed through observational data gathered during on-site inspection and is verified through numerical simulations using the impact of the main shock of 26th of November 2019. The technical efficiency and the economic feasibility of seismic isolation through pendulum isolators for non-strategic buildings are analysed by designing, detailing, and estimating the cost of an ordinary six story residential building in both approaches (capacity design and seismic isolation approach), accounting for the local factors such as typical architecture, soil characterization, the level of the seismic hazard and the cost of the construction materials and workmanship in the densely populated region of central Albania. The structural behaviour assessment highlighted the superiority of the seismic isolation, and the feasibility analysis revealed that the initial cost for delivering seismically isolated buildings is higher than that of the conventional approach by some percentage. However, the gains in structural performance and the relatively high seismic hazard of the country make seismic isolation of buildings a relevant alternative in achieving seismic protection for buildings in the region.
Markdown Optimization with Generalized Weighted Least Squares Estimation
Authors: Hekimoğlu Mustafa
Published: December 2022

Retailers increasingly apply price markdowns for their seasonal products. Efficiency of these markdown applications is driven by the accuracy of empirical models, especially toward the end of a selling season. In the literature, recent sales are recognized to be more important than older sales data for estimating the current period’s demand for a given markdown level. The importance difference between the weeks of a selling season is addressed by weighted least squares (WLS) method with continuous weight functions of time. This study suggests a generalization of the weight functions and a method for optimizing their shape and discretization parameters to stimulate the predictive accuracy of models. We find that addressing the importance difference of recent sales observations using our generalized weight functions improves the forecast accuracy by up to 20%, and most of the improvement stems from our weight discretization method.
Large-Scale Convex Optimization
Authors: Ryu Ernest K. , Yin Wotao
Published: December 2022

Starting from where a first course in convex optimization leaves off, this text presents a unified analysis of first-order optimization methods – including parallel-distributed algorithms – through the abstraction of monotone operators. With the increased computational power and availability of big data over the past decade, applied disciplines have demanded that larger and larger optimization problems be solved. This text covers the first-order convex optimization methods that are uniquely effective at solving these large-scale optimization problems. Readers will have the opportunity to construct and analyze many well-known classical and modern algorithms using monotone operators, and walk away with a solid understanding of the diverse optimization algorithms. Graduate students and researchers in mathematical optimization, operations research, electrical engineering, statistics, and computer science will appreciate this concise introduction to the theory of convex optimization algorithms.
Gillespie Algorithms for Stochastic Multiagent Dynamics in Populations and Networks
Authors: Masuda Naoki , Vestergaard Christian L.
Published: December 2022

Many multiagent dynamics can be modeled as a stochastic process in which the agents in the system change their state over time in interaction with each other. The Gillespie algorithms are popular algorithms that exactly simulate such stochastic multiagent dynamics when each state change is driven by a discrete event, the dynamics is defined in continuous time, and the stochastic law of event occurrence is governed by independent Poisson processes. The first main part of this volume provides a tutorial on the Gillespie algorithms focusing on simulation of social multiagent dynamics occurring in populations and networks. The authors clarify why one should use the continuous-time models and the Gillespie algorithms in many cases, instead of easier-to-understand discrete-time models. The remainder of the Element reviews recent extensions of the Gillespie algorithms aiming to add more reality to the model (i.e., non-Poissonian cases) or to speed up the simulations. This title is also available as open access on Cambridge Core.
Unseen and Everyday: International Secretariats Under the Spotlight
Authors: Sinclair Guy Fiti
Published: December 2022

In “Who Guards the ‘Guardians of the System’? The Role of the Secretariat in WTO Dispute Settlement,” Joost Pauwelyn and Krzysztof Pelc make a novel and compelling contribution to our understanding of the secretariat of the World Trade Organization (WTO). Firmly grounded in principal-agent theory, their analysis nonetheless resonates with—and thereby suggests some advantages in pursuing—other, complementary approaches that probe the complex inner lives of the secretariats of international organizations. Two central themes in the article, one explicit and the other implicit, provide especially useful entry points for comparison and linkages with these other approaches. In exploring these themes, this Essay aims to build on Pauwelyn and Pelc's insightful work to encourage more critical engagement with the governance dynamics and effects of international secretariats.
The unique value proposition for using drones to map coastal ecosystems
Authors: Joyce Karen E. , Fickas Kate C. , Kalamandeen Michelle
Published: December 2022

Coasts are undeniably regions of critical importance for a range of environmental, sociocultural, and economic reasons. Yet they are also areas of intense anthropogenic impact and are particularly susceptible to climate change-related concerns. As such, it is imperative that we have the means to monitor and manage them in a sustainable manner. Drone technology has emerged as providing a unique value proposition in coastal environments to support data-driven monitoring and management decisions. With their highly detailed data capture capability, drones are particularly well suited to map the spatial heterogeneity, structural complexity, and temporally dynamic nature of coasts. Further, they are readily accessible to coastal populations and can promote grassroots action by the very people whose lives and livelihoods the coasts support. Herein, we cover several of the leading innovations in using aerial drones to map coastal ecosystems. We then consider how general trends and technology projections including artificial intelligence, as well as cloud and edge computing offer opportunities for the future of drone mapping and monitoring in a coastal context. While the challenge of change is inevitable, embracing the opportunities it provides will allow us to better understand and live sustainably with and within our coastal ecosystems.
The evolution of 21st century sea-level projections from IPCC AR5 to AR6 and beyond
Authors: Slangen Aimée B.A. , Palmer Matthew D. , Camargo Carolina M.L. , Church John A. , Edwards Tamsin L. , Hermans Tim H.J. , Hewitt Helene T. , Garner Gregory G. , Gregory Jonathan M. , Kopp Robert E. , Santos Victor Malagon , van de Wal Roderik S.W.
Published: December 2022

Sea-level science has seen many recent developments in observations and modelling of the different contributions and the total mean sea-level change. In this overview, we discuss (1) the evolution of the Intergovernmental Panel on Climate Change (IPCC) projections, (2) how the projections compare to observations and (3) the outlook for further improving projections. We start by discussing how the model projections of 21st century sea-level change have changed from the IPCC AR5 report (2013) to SROCC (2019) and AR6 (2021), highlighting similarities and differences in the methodologies and comparing the global mean and regional projections. This shows that there is good agreement in the median values, but also highlights some differences. In addition, we discuss how the different reports included high-end projections. We then show how the AR5 projections (from 2007 onwards) compare against the observations and find that they are highly consistent with each other. Finally, we discuss how to further improve sea-level projections using high-resolution ocean modelling and recent vertical land motion estimates.
Importance of Identifying Cognitive Impairment in Multiple Sclerosis
Authors: Morrow Sarah A. , Baldwin Chantal , Alkabie Samir
Published: December 2022

This article aims to highlight the impact of cognitive impairment on outcomes and quality of life for people with multiple sclerosis (MS) and to review current evidence for the efficacy of disease-modifying therapies (DMTs) and other interventions. In addition, we provide clinical practice insights regarding screening and management of cognitive impairment in people with MS. Evidence suggests that cognitive deterioration often accompanies magnetic resonance imaging changes. Neocortical volume and deep grey matter atrophy correlate with cognitive impairment. Similarly, cognitive decline is predictive of a higher lesion burden. Cognitive impairment is an important clinical measure of disability and negatively impacts quality of life. Phase 3 studies suggest that DMTs such as natalizumab, ozanimod and fingolimod may provide long-lasting, clinically meaningful effects on cognition in people with MS. Further data are needed to support the use of adjunct cognitive behavioural and exercise interventions for people with MS who have cognitive impairment. More data are needed to define appropriate management strategies for cognitive impairment in people with MS. Baseline and periodic screening for cognitive impairment and inclusion of cognitive impairment as a clinical trial endpoint will help to inform efforts to manage this important aspect of MS.
AXIOMATIZATIONS OF PEANO ARITHMETIC: A TRUTH-THEORETIC VIEW
Authors: ENAYAT ALI , ŁEŁYK MATEUSZ
Published: December 2022

We employ the lens provided by formal truth theory to study axiomatizations of Peano Arithmetic
${\textsf {(PA)}}$
. More specifically, let Elementary Arithmetic
${\textsf {(EA)}}$
be the fragment
$\mathsf {I}\Delta _0 + \mathsf {Exp}$
of
${\textsf {PA}}$
, and let
${\textsf {CT}}^-[{\textsf {EA}}]$
be the extension of
${\textsf {EA}}$
by the commonly studied axioms of compositional truth
${\textsf {CT}}^-$
. We investigate both local and global properties of the family of first order theories of the form
${\textsf {CT}}^-[{\textsf {EA}}] +\alpha $
, where
$\alpha $
is a particular way of expressing “
${\textsf {PA}}$
is true” (using the truth predicate). Our focus is dominantly on two types of axiomatizations, namely: (1) schematic axiomatizations that are deductively equivalent to
${\textsf {PA}}$
and (2) axiomatizations that are proof-theoretically equivalent to the canonical axiomatization of
${\textsf {PA}}$
.
Degradation or Redemption? A Parole Board Polices a Moral Boundary
Authors: Herbert Steve
Published: December 2022

Parole boards possess the notable power to grant release from prison, oftentimes well short of an incarcerated person’s legally allowable length of sentence. Although the exercise of that power is, at least in part, governed by law, extralegal considerations likely play an influential part in decisions to grant release. Indeed, the analysis offered here of parole board hearings in Washington State reveals, in particular, the work that board members perform to reinforce the moral significance of past criminality. In parole hearings, board members find ample opportunities to morally condemn the index offenses that petitioners have perpetrated and to express skepticism about narratives of petitioner change. Instead of helping petitioners ease the burden of being profaned for their past acts, board members often act to reinforce the mark of a criminal record. These realities underscore the significant work necessary to shift attitudes toward those convicted of crimes, and expose the cultural challenges that attempts to reduce incarceration more generally are likely to face, especially in the United States.
Food, health and sustainability
Authors: Jackson Peter
Published: December 2022

This review makes a case for taking an integrated ‘food systems’ approach to explore the links between health and sustainability rather than treating them as separate topics. Unlike more linear ‘farm-to-fork’ conceptions, a systems approach emphasises the links between domains and sectors, helping avoid perverse effects where an intervention at one point in the system can have unanticipated consequences at other points. Adopting this approach, the review argues that food security and sustainability are as much a socio-cultural as a technical challenge requiring the combined forces of researchers from the natural and social sciences together with a range of stakeholders from government, business and civil society. Meeting the twin challenges of health and sustainability will require changes to intensive food production systems, dietary change and reductions in current levels of food waste. The review explores why dietary practices are so resistant to change seeking alternatives to the deficit thinking that pervades much advice on ‘healthy eating’. It explores the locus of responsibility for food system change, emphasising the asymmetrical power relations that shape contemporary dietary choices. The review includes an example of food system research, the H3 project (healthy soil, healthy food, healthy people), which seeks to transform UK food systems ‘from the ground up’, adopting the principles outlined in the body of the review.
Parrot population trends in Nicaragua revealed by long-term monitoring
Authors: Hille David C. , Wiedenfeld David A. , Lezama-López Martín , Brightsmith Donald J. , Patten Michael A.
Published: December 2022

Monitoring parrot populations is of high importance because there is a general lack of quantified population trends for one of the most threatened avian orders. We surveyed parrots in Nicaragua in 1995, 1999, 2004, and 2013 at a minimum of 227 points within 56 sites stratified among the Pacific, Central Highlands, and Caribbean biogeographical regions to assess population trends. From point-count data we calculated encounter rate, flock rate, and flock size metrics and we used presence/absence data to generate species-specific occupancy estimates. Encounter rate, flock rate, and flock size data suggested family-level declines from 1995 to 2004 with some recovery between 2004 and 2013. Patterns of parrot occupancy varied among species with four decreasing, five increasing, and two with no detectable change. Six species of conservation concern are identified, including the Critically Endangered Great Green Macaw and Yellow-naped Parrot, additionally Olive-throated Parakeet, Scarlet Macaw, Brown-hooded Parrot, and White-crowned Parrot, only listed as Least Concern. All six are likely suffering from deforestation and potential unchecked trade activity in the Caribbean. Differing population trends of the regionally disjunct Yellow-naped Parrot subspecies suggest a link to variable deforestation and trade pressure experienced between the Pacific and Caribbean. Our results highlight the importance of actively monitoring changing parrot populations, even when considered Least Concern, so that directed conservation actions can be taken if needed.
Pre-welfare state provision and adverse selection: enrolment in a Swedish nationwide health insurance society
Authors: Andersson Lars Fredrik , Eriksson Liselotte , Lilljegren Josef
Published: December 2022

Mutual benefit societies evolved as the major provider for sickness, accident and life insurance in the late nineteenth and early twentieth centuries on both sides of the Atlantic. One of the major problems facing insurers was the risk of adverse selection, i.e. that unhealthy individuals had more incentives than healthy individuals to insure when priced for the average risk. By empirically examining whether longevity among insured individuals in a nationwide mutual health society was different from a matched sample of uninsured individuals, we seek to identify the presence of adverse selection. We find no compelling evidence showing that unhealthy individuals were more likely to insure, or reasons to believe that problems related to adverse selection would have been a major reason for government intervention in the health insurance market in Sweden.
Linguistic factors modulating gender assignment in Spanish–English bilingual speech
Authors: Cruz Abel
Published: December 2022

Drawing on naturally-occurring bilingual speech from a well-defined codeswitching community in Southern Arizona, this study examined the influence of semantic gender (a.k.a. biological gender), analogical gender, and other-language phonemic cues in modulating gender assignment in Spanish–English codeswitched speech. Thirty-four Spanish–English early bilinguals completed a forced-choice elicitation task involving two codeswitching environments: Spanish determiner–English noun switches (Task 1) and English–Spanish switched copula constructions (Task 2). The results revealed that for human-denoting nouns, bilinguals assigned grammatical gender based on the presupposed sex of a noun's referent in both syntactic environments tested. As for inanimate nouns, bilinguals were more likely to assign masculine over feminine gender to such nouns in determiner–noun switches, but not in switched copula constructions. Other-language phonemic cues did not influence the assignment mechanism. A methodological implication is that the study replicated the codeswitching patterns observed in naturally-occurring bilingual speech from the same bilingual community.
A New Simulation‐Optimization Framework for Estimation of Submarine Groundwater Discharge Based on Hydrodynamic Modeling and Isotopic Data
Authors: Yu Jiang , Tian Yong , Wang Xuejing , Wang Xiaoli , Lancia Michele , Li Hailong , Andrews Charles B. , Zheng Chunmiao
Published: December 2022

Estimation of submarine groundwater discharge (SGD) has traditionally relied on isotopic mass balance calculation, which is a form of regional box modeling. Here we propose an entirely new approach for SGD estimation by coupling a general marine hydrodynamic simulation model with a global optimization algorithm. The newly developed approach has the distinct advantage of being able to map the spatial distribution of SGD. The results of this study show that the estimated SGD exhibits a strong spatial heterogeneity. Furthermore, this study underscores the important effect of isotope sampling time on SGD estimation. Thus, the fluctuation of seawater level and a lag in isotope activity to water level changes should be considered to assure the representativeness of measured isotope activities. Overall, the simulation‐optimization framework adds a versatile and powerful new tool for more detailed SGD estimation.
Juno's Close Encounter With Ganymede—An Overview
Authors: Hansen C. J. , Bolton S. , Sulaiman A. H. , Duling S. , Bagenal F. , Brennan M. , Connerney J. , Clark G. , Lunine J. , Levin S. , Kurth W. , Mura A. , Paranicas C. , Tosi F. , Withers P.
Published: December 2022

The Juno spacecraft has been in orbit around Jupiter since 2016. Two flybys of Ganymede were executed in 2021, opportunities realized by evolution of Juno's polar orbit over the intervening 5 years. The geometry of the close flyby just prior to the 34th perijove pass by Jupiter brought the spacecraft inside Ganymede's unique magnetosphere. Juno's payload, designed to study Jupiter's magnetosphere, had ample dynamic range to study Ganymede's magnetosphere. The Juno radio system was used both for gravity measurements and for study of Ganymede's ionosphere. Remote sensing of Ganymede returned new results on geology, surface composition, and thermal properties of the surface and subsurface.
Gravity Field of Ganymede After the Juno Extended Mission
Authors: Gomez Casajus L. , Ermakov A. I. , Zannoni M. , Keane J. T. , Stevenson D. , Buccino D. R. , Durante D. , Parisi M. , Park R. S. , Tortora P. , Bolton S. J.
Published: December 2022

The Juno Extended Mission presented the first opportunity to acquire gravity measurements of Ganymede since the end of the Galileo mission. These new Juno data offered the chance to carry out a joint analysis with the Galileo data set, improving our knowledge of Ganymede's gravity field and shedding new light upon its interior structure. Through reconstruction of Juno's and Galileo's orbit during the Ganymede flybys, the gravity field of the moon was estimated. The results indicate that Ganymede's degree‐2 field is compatible with a body in hydrostatic equilibrium within 1−𝜎 and hint at regional gravity anomalies with amplitudes exceeding those inferred by Cassini for Titan. Our explicit treatment of non‐hydrostatic effects leads to wider confidence intervals for the derived moment of inertia with respect previous analyses. The higher central value of the derived moment of inertia indicates a lesser degree of Ganymede's differentiation.
Juno Magnetometer Observations at Ganymede: Comparisons With a Global Hybrid Simulation and Indications of Magnetopause Reconnection
Authors: Romanelli N. , DiBraccio G. A. , Modolo R. , Connerney J. E. P. , Ebert R. W. , Martos Y. M. , Weber T. , Espley J. R. , Kurth W. S. , Allegrini F. , Valek P. , Bolton S. J.
Published: December 2022

Juno's flyby of Ganymede on 7 June 2021, provides a unique opportunity to explore the moon's magnetosphere. By means of Magnetometer (MAG) observations and a hybrid numerical simulation, we provide a global description of this environment and analyze the upstream magnetopause in detail. In particular, LATMOS Hybrid Simulation results suggest Juno observed open (one foot‐point connected to Ganymede, the other to Jupiter) and closed magnetic field lines along its trajectory. Additionally, we determine that the upstream magnetopause location and orientation seen by Juno MAG are consistent with previous Galileo observations. We observe a non‐zero normal component of the magnetic field across the magnetopause, along with flux rope structures embedded within the boundary's current sheet. Both signatures are strong indicators that magnetic reconnection was occurring along Ganymede's magnetopause. Based on this crossing, we calculate a shear angle of ∼78° across the magnetopause, with a dimensionless reconnection rate of ∼0.12.
Forecasting of in situ electron energy loss spectroscopy
Authors: Lewis Nicholas R. , Jin Yicheng , Tang Xiuyu , Shah Vidit , Doty Christina , Matthews Bethany E. , Akers Sarah , Spurgeon Steven R.
Published: December 2022

Forecasting models are a central part of many control systems, where high-consequence decisions must be made on long latency control variables. These models are particularly relevant for emerging artificial intelligence (AI)-guided instrumentation, in which prescriptive knowledge is needed to guide autonomous decision-making. Here we describe the implementation of a long short-term memory model (LSTM) for forecasting in situ electron energy loss spectroscopy (EELS) data, one of the richest analytical probes of materials and chemical systems. We describe key considerations for data collection, preprocessing, training, validation, and benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation.
Prediction study of electric energy production in important power production base, China
Authors: Zhu XiXun , Song Zhixin , Sen Gan , Tian Maozai , Zheng Yanling , Zhu Bing
Published: December 2022

Xinjiang is an important power production base in China, and its electric energy production needs not only meet the demand of Xinjiang's electricity consumption, but also make up for the shortage of electricity in at least 19 provinces or cities in China. Therefore, it is of great significance to know ahead of time the electric energy production of Xinjiang in the future. In such terms, accurate electric energy production forecasts are imperative for decision makers to develop an optimal strategy that includes not only risk reduction, but also the betterment of the economy and society as a whole. According to the characteristics of the historical data of monthly electricity generation in Xinjiang from January 2001 to August 2020 , the suitable and widely used SARIMA (Seasonal autoregressive integrated moving mean model) method and Holt-winter method were used to construct the monthly electric energy production in Xinjiang for the first time. The results of our analysis showed that the established SARIMA((1,2,3,4,6,7,11),2,1)(1,0,1)12model had higher prediction accuracy than that of the established Holt-Winters' multiplicative model. We predicted the monthly electric energy production from August 2021 to August 2022 by the SARIMA((1,2,3,4,6,7,11),2,1)(1,0,1)12model, and errors are very small compared to the actual values, indicating that our model has a very good prediction performance. Therefore, based on our study, we provided a simple and easy scientific tool for the future power output prediction in Xinjiang. Our research methods and research ideas can also provide scientific reference for the prediction of electric energy production elsewhere.
Crown feature effect evaluation on wind load for evergreen species based on laser scanning and wind tunnel experiments
Authors: Lai Can , Xiao Bing , Feng Jialang , Wang Longyuan , Zhang Yanjing , Sun Yanjun , Chen Xiaoxi , Guo Wei
Published: December 2022

The wind load a tree withstood is mainly applied to its crown, whose morphology and structure directly affect the degree of wind load given a certain wind condition. Though the features of tree crown are relatively easy to measure, however, among them which is/are the determining factor and how they contribute to wind load remain unknown. In order to figure out how crown features of different tree species influence the wind load, the wind tunnel experiment was performed for 7 most used urban greening tree species, and laser scanning was used to measure the accurate crown features. The results derived by multiple linear model showed (1) Ficus concinna, Dracontomelon duperreanum, Ormosia pinnata and Bischofia javanica are recommended in urban greening for suffering the smaller wind load under the same conditions, whereas Schefflera macrostachya, Acacia confusa and Khaya senegalensis are inadequate towards the view of crown features; (2) crown features like crown horizontal ratio, windward side projection and porosity ratio are important in estimating wind load. Our study demonstrated that evaluating the wind load via crown features is feasible, and provided valuable suggestion for selecting idealized decorative trees in urban environment with a smaller wind load due to the crown features.
Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology
Authors: Ansari Kutubuddin , Seok Hong-Woo , Jamjareegulgarn Punyawi
Published: December 2022

The tide gauge measurements from global navigation satellite system reflectometry (GNSS-R) observables are considered to be a promising alternative to the traditional tide gauges in the present days. In the present paper, we deliver a comparative analysis of tide-gauge (TG) measurements retrieved by quasi-zenith satellite system-reflectometry (QZSS-R) and the legacy TG recordings with additional observables from other constellations viz. GPS-R and GLONASS-R. The signal-to-noise ratio data of QZSS (L1, L2, and L5 signals) retrieved at the P109 site of GNSS Earth Observation Network in Japan (37.815° N; 138.281° E; 44.70 m elevation in ellipsoidal height) during 01 October 2019 to 31 December 2019. The results from QZSS observations at L1, L2, and L5 signals show respective correlation coefficients of 0.8712, 0.6998, and 0.8763 with observed TG measurements whereas the corresponding root means square errors were 4.84 cm, 4.26 cm, and 4.24 cm. The QZSS-R signals revealed almost equivalent precise results to that of GPS-R (L1, L2, and L5 signals) and GLONASS-R (L1 and L2 signals). To reconstruct the tidal variability for QZSS-R measurements, a machine learning technique, i.e., kernel extreme learning machine (KELM) is implemented that is based on variational mode decomposition of the parameters. These KELM reconstructed outcomes from QZSS-R L1, L2, and L5 observables provide the respective correlation coefficients of 0.9252, 0.7895, and 0.9146 with TG measurements. The mean errors between the KELM reconstructed outcomes and observed TG measurements for QZSS-R, GPS-R, and GLONASS-R very often lies close to the zero line, confirming that the KELM-based estimates from GNSS-R observations can provide alternative unbiased estimations to the traditional TG measurement. The proposed method seems to be effective, foreseeing a dense tide gauge estimations with the available QZSS-R along with other GNSS-R observables.
Operating region-dependent characteristics of weight updates in synaptic In–Ga–Zn–O thin-film transistors
Authors: Cha Danyoung , Kang Yeonsu , Lee Sungsik
Published: December 2022

We present a study on characteristics of operating region-dependent weight updates in a synaptic thin-film transistor (Syn-TFT) with an amorphous In–Ga–Zn–O (IGZO) channel layer. For a synaptic behavior (e.g. a memory phenomenon) of the IGZO TFT, a defective oxide (e.g. SiO2) is intentionally used for a charge trapping due to programming pulses to the gate terminal. Based on this synaptic behavior, a conductance of the Syn-TFT is modulated depending on the programming pulses, thus weight updates. This weight update characteristics of the Syn-TFT is analyzed in terms of a dynamic ratio (drw) for two operating regions (i.e. the above-threshold and sub-threshold regimes). Here, the operating region is chosen depending on the level of the gate read-voltage relative to the threshold voltage of the Syn-TFT. To verify these, the static and pulsed characteristics of the fabricated Syn-TFT are monitored experimentally. As experimental results, it is found that the drw of the sub-threshold regime is larger compared to the above-threshold regime. In addition, the weight linearity in the sub-threshold regime is observed to be better compared to the above-threshold regime. Since it is expected that either the drw or weight linearity can affect performances (e.g. a classification accuracy) of an analog accelerator (AA) constructed with the Syn-TFTs, the AA simulation is performed to check this with a crossbar simulator.
L1CAM is required for early dissemination of fallopian tube carcinoma precursors to the ovary
Authors: Doberstein Kai , Spivak Rebecca , Reavis Hunter D. , Hooda Jagmohan , Feng Yi , Kroeger Paul T. , Stuckelberger Sarah , Mills Gordon B. , Devins Kyle M. , Schwartz Lauren E. , Iwanicki Marcin P. , Fogel Mina , Altevogt Peter , Drapkin Ronny
Published: December 2022

Most ovarian high-grade serous carcinomas (HGSC) arise from Serous Tubal Intraepithelial Carcinoma (STIC) lesions in the distal end of the fallopian tube (FT). Formation of STIC lesions from FT secretory cells leads to seeding of the ovarian surface, with rapid tumor dissemination to other abdominal structures thereafter. It remains unclear how nascent malignant cells leave the FT to colonize the ovary. This report provides evidence that the L1 cell adhesion molecule (L1CAM) contributes to the ability of transformed FT secretory cells (FTSEC) to detach from the tube, survive under anchorage-independent conditions, and seed the ovarian surface. L1CAM was highly expressed on the apical cells of STIC lesions and contributed to ovarian colonization by upregulating integrins and fibronectin in malignant cells and activating the AKT and ERK pathways. These changes increased cell survival under ultra-low attachment conditions that mimic transit from the FT to the ovary. To study dissemination to the ovary, we developed a tumor-ovary co-culture model. We showed that L1CAM expression was important for FT cells to invade the ovary as a cohesive group. Our results indicate that in the early stages of HGSC development, transformed FTSECs disseminate from the FT to the ovary in a L1CAM-dependent manner.
First ternary tungsten tellurate(iv) WTe2O7 with unique crystal structure type
Authors: Ziegler Raimund , Purtscher Felix R. S. , Schwartz Heidi A. , Hofer Thomas S. , Heymann Gunter
Published: December 2022

High-pressure/high-temperature synthesis of the first tungsten oxotellurate compound with Te4+ lone-pair electrons and unique crystal structure.
Tightly intercalated Ti3C2Tx/MoO3−x/PEDOT:PSS free-standing films with high volumetric/gravimetric performance for flexible solid-state supercapacitors
Authors: Zhang Pengxue , Sui Yan , Ma Weijing , Duan Nannan , Liu Qi , Zhang Bingmiao , Niu Haijun , Qin Chuanli
Published: December 2022

Tightly intercalated Ti3C2Tx/MoO3−x/PEDOT:PSS free-standing films for flexible solid-state symmetrical supercapacitors exhibit high volumetric/gravimetric capacitance and rate performance.
Probing the limits of sampling gaseous elemental mercury passively in the remote atmosphere
Authors: Hoang Christopher , Magand Olivier , Brioude Jérôme , Dimuro Andrea , Brunet Christophe , Ah-Peng Claudine , Bertrand Yann , Dommergue Aurelien , Lei Ying Duan , Wania Frank
Published: December 2022

Deployment times of at least two months and dedicated efforts to limit contamination during transport and storage are recommended to obtain reliable results when using passive samplers for gaseous elemental mercury in the remote atmosphere.